Intelligent pre-control platform for state of gas generator set equipment

Abstract

The application discloses a kind of gas generator set equipment state intelligent pre-control platform, it is related to the monitoring and control technical field of gas generator set, including: data acquisition layer, signal processing and feature extraction layer, multi-physical field coupling modeling layer, parameter inversion and compensation layer, intelligent pre-control decision layer and user interaction layer;In the application, aerodynamics-mechanical vibration signal separation is realized by multimodal decoupling algorithm, combined with the real-time processing architecture based on XilinxZynqFPGA, solves the nonlinear error problems such as material creep, turbulent boundary layer separation that traditional model cannot capture, realizes that multi-physical field coupling model prediction error is optimized from 4.5%RMS to 1.8%RMS, and compensation efficiency reaches 66.9%-71.5% in 30%-100% load range, and comprehensive performance reaches industry leading level.

Description

Technical Field

[0001] This invention relates to the field of monitoring and control technology for gas generator sets, specifically to an intelligent pre-control platform for the status of gas generator set equipment. Background Technology

[0002] The operation of gas turbine generator sets involves strong coupling effects from multiple physical fields, including aerodynamics, thermodynamics, electromagnetics, and mechanical vibration. Traditional pre-control models typically employ simplified linearization assumptions, making it difficult to accurately describe the complex nonlinear behavior under high-temperature and high-pressure environments. In actual operation, the following unstructured errors significantly affect model accuracy:

[0003] Material creep and time-varying parameters

[0004] The creep behavior of materials at high temperatures exhibits nonlinear characteristics. For example, during long-term service, when the temperature of a turbine blade exceeds one-third of the material's melting point, grain boundary slip and dislocation movement lead to continuous plastic deformation. Its creep curve goes through three stages: initial deceleration, steady state, and accelerated fracture. [1] This type of time-varying behavior causes asymptotic drift in model parameters (such as elastic modulus and damping coefficient). Related studies have shown that the compensation efficiency of traditional linear compensation strategies in the nonlinear region Δf∈(0.5fn,1.5fn) is less than 50%. [2] It cannot suppress the accumulation of errors caused by time-varying parameters.

[0005] Turbulent boundary layer separation and flow unsteadiness

[0006] Turbulent boundary layer separation on compressor blade surfaces and thermoacoustic oscillations in the combustion chamber lead to strongly nonlinear gas flow characteristics. For example, under low turbulence, laminar separation bubbles easily form on the suction surface of a high-load compressor, while high turbulence or changes in Reynolds number can alter the transition position and affect flow stability. [3] The nonlinear oscillation phenomenon induced by the coupling of acoustic, thermal, and fluid fields in the combustion chamber makes it difficult for traditional simplified models based on the Navier-Stokes equations to accurately capture the dynamic response of the flow field, resulting in increased prediction errors. [4] .

[0007] Nonlinear vibration mode coupling

[0008] The vibration modes of rotor systems under varying load conditions are prone to nonlinear coupling, such as the interaction of bending and torsional vibrations in the shaft system, and the aeroelastic coupling between the blades and the airflow. Experimental data show that traditional dynamic models based on the superposition of linear modes can get trapped in local optima under complex excitations. [5] It cannot effectively describe the abrupt changes in vibration response caused by multimodal coupling.

[0009] Citations

[0010] [1] Creep Curve (a curve describing the creep process of metallic materials) (Douyin Encyclopedia, published before March 18, 2025)

[0011] [2] "Creep Damage Behavior of Turbine Blades and the Influence of Solution Treatment on Blade Material Properties" (CNKI, published before March 18, 2025)

[0012] [3] Thermoacoustic oscillations in the combustion chamber (Yearbook of Fluid Mechanics, October 2024)

[0013] [4] Vibration and Shock (Vol. 53, No. 12, 2017)

[0014] [5] Research on Denoising Method of Vibration Signal of Rotating Machinery Based on EMD and Wavelet Transform (Journal of Mechanical Engineering, Vol. 53, No. 12, 2017).

[0015] In view of this, an intelligent pre-control platform for the status of gas generator sets is provided to overcome the above problems. Summary of the Invention

[0016] The purpose of this invention is to provide an intelligent pre-control platform for the status of gas generator sets, so as to solve the problems mentioned in the background art.

[0017] To solve the above-mentioned technical problems, the present invention provides an intelligent pre-control platform for the status of gas generator sets, comprising:

[0018] Data acquisition layer: Equipped with: a bidirectional acceleration sensor at the turbine rotor journal, a high-temperature strain gauge sensor at the cylinder flange, a magnetoelectric speed sensor at the bearing housing; and a 10kHz sampling interface synchronized with the DCS system;

[0019] Signal processing and feature extraction layer: includes: an improved Hilbert-Huang transform algorithm module, integrating endpoint extension, empirical mode decomposition and Hilbert transform; and a multi-mode decoupling unit based on improved independent component analysis;

[0020] Multiphysics coupling modeling layer: Constructs multiphysics coupling models of thermodynamics, fluid mechanics, electromagnetics, and structural mechanics; as well as coupling interfaces of heat-fluid, fluid-solid, and electro-magnetic-mechanical.

[0021] Parameter inversion and compensation layer: including: a thermodynamic parameter inversion module based on orthogonal experimental design; and an adaptive compensation unit driven by fuzzy logic;

[0022] Intelligent pre-control decision layer: integrates fault prediction model based on machine learning algorithm and dynamic control strategy generation module.

[0023] Furthermore, it also includes:

[0024] User interaction layer: includes: real-time status visualization interface; and parameter setting and strategy adjustment interface.

[0025] Furthermore, the modal decoupling unit adopts an improved ICA algorithm based on the natural gradient descent method, sets a maximum number of iterations of 500 and a convergence threshold of 1e-6, and combines a frequency masking mechanism to suppress high-frequency noise for the separation of electromagnetic interference and mechanical vibration signals.

[0026] Furthermore, the signal processing module is built on a three-stage pipeline architecture based on Xilinx Zynq FPGA, which includes parallel processing units for endpoint extension, EMD decomposition, and Hilbert transform. The single-frame signal processing latency is ≤5ms, and it supports 128 channels of synchronous processing.

[0027] Furthermore, the thermodynamic parameter inversion module establishes a mapping relationship between frequency shift and gas path parameters:

[0028] Δf=α·ΔT+β·Δρ+γ·∈,

[0029] The coefficient matrix is ​​determined by using, but not limited to, L9(3^4) orthogonal arrays, and the model parameters are corrected by combining real-time temperature data from the DCS.

[0030] Furthermore, nonlinear compensation is triggered when the frequency offset Δf∈(0.5f_n,1.5f_n), otherwise linear compensation is used.

[0031] Furthermore, the robustness enhancement module includes:

[0032] Iterative weighted least squares robust estimation unit: zero weight is assigned to observations with residual absolute value > 3 times the standard error;

[0033] Adaptive frequency-locked loop combining second-order phase-locked loop and Kalman filter: frequency tracking deviation ≤ ±0.1Hz.

[0034] Furthermore, the parameters of the multiphysics coupling model are optimized using a particle swarm optimization algorithm.

[0035] Furthermore, the empirical mode decomposition module in the signal processing and feature extraction layer fits the local maxima and minima of the signal through cubic spline interpolation, generates upper and lower envelopes, and iteratively updates them to satisfy the intrinsic mode function conditions for the decomposition of multi-scale vibration signals.

[0036] Furthermore, the multiphysics coupling modeling layer maps the thermodynamic temperature distribution to fluid density and viscosity parameters through the thermal-fluid coupling interface, transmits fluid pressure to the structural mechanical load boundary conditions through the fluid-structure coupling interface, and feeds back electromagnetic torque to the rotor dynamics equations through the electro-magnetic-mechanical coupling interface, enabling bidirectional data interaction between interdisciplinary fields.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] Precise decoupling of multiple signals

[0039] By employing an improved Independent Component Analysis (ICA) algorithm combined with a frequency masking mechanism, an effective signal energy ratio of ≥98.1% (Table 2) is achieved in composite signals of multi-source electromagnetic interference and mechanical vibration. The signal-to-noise ratio of the vibration signal is improved by 6dB (the ratio of original noise energy to signal energy is 1:10), which solves the feature extraction distortion problem caused by signal coupling in traditional methods.

[0040] Breakthrough in real-time processing architecture

[0041] A three-stage pipelined parallel processing system based on Xilinx Zynq FPGA was built, with single-frame signal processing latency stably controlled at 4.3-4.6ms (Table 3). It supports 128 channels of synchronous processing, meets the online analysis requirements of 10kHz sampling rate, and improves real-time performance by more than 60% compared with traditional ARM architecture (latency 12-15ms).

[0042] Parameter dynamic compensation improvement

[0043] A mapping relationship between thermodynamic parameters and frequency shift was established. The coefficient matrix was optimized using an L9(3^4) orthogonal array, and combined with fuzzy logic compensation rules, reducing the model prediction error from 3.2% RMS to 0.9% RMS (Table 1), with an average compensation efficiency of 70.1%. In the nonlinear region Δf∈(0.5fn,1.5fn), the compensation efficiency was improved by 40% compared to the traditional linear strategy (less than 50%).

[0044] Robustness enhancement strategies

[0045] Robust estimation: Iterative weighted least squares method to handle outliers reduced the prediction error from 2.56% RMS to 1.34% (Table 4), and the parameter estimation bias was reduced by 68%.

[0046] Adaptive frequency-locked loop: A second-order phase-locked loop combined with Kalman filtering achieves a tracking deviation of ≤±0.09Hz in frequency step scenarios (Table 5), which is 55% more accurate than the traditional phase-locked loop (deviation ±0.2Hz).

[0047] Improved adaptability to operating conditions

[0048] By optimizing the parameters of the multiphysics model using the particle swarm optimization (PSO) algorithm, the standard deviation of the prediction error was reduced by more than 40% (Table 6). The compensation efficiency remained stable at 66.9%-71.5% within the 30%-100% load range, effectively addressing the strong nonlinear dynamic response under variable load conditions.

[0049] Engineering application value

[0050] Based on the existing sensor configuration, the algorithm was upgraded, and the generalization ability of the multiphysics model was improved by 43.75% through PSO optimization (Table 6), reducing the system transformation cost by 60%.

[0051] The real-time processing latency is <5ms, meeting the requirements of the ISO10816 vibration monitoring standard. The accuracy of nonlinear vibration mode identification is improved by 27%, and the misjudgment rate of turbulent boundary layer separation is reduced from 18% to 4% (Table 7).

[0052] The aforementioned technological breakthroughs enabled the platform to reduce the mean prediction error of the multiphysics coupling model from 4.5% RMS to 1.8% RMS in actual tests on a 100MW gas turbine (Table 6), achieving industry-leading overall performance. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of an intelligent pre-control platform for the status of a gas generator set according to the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1 The present invention provides a technical solution:

[0056] See Figure 1 As shown, an embodiment of an intelligent pre-control platform for the status of gas generator sets is provided:

[0057] I. System Architecture

[0058] 1. Data Acquisition Layer

[0059] Sensor placement:

[0060] Two PCB352C33 accelerometers are installed at the turbine rotor journal to monitor vibrations in the X and Y directions, respectively. They have a sensitivity of 100 mV / g and a frequency response range of 0.5-10 kHz, enabling precise capture of torsional and bending vibrations of the rotor. A magnetic mounting base ensures a dynamic response error of <0.5%.

[0061] Three HBMU9C high-temperature strain gauge sensors with a gauge length of 3mm and a strain range of ±2% are arranged at the cylinder flange position to monitor modal changes caused by thermal expansion.

[0062] It should be further explained that the above parameters are the technical specifications of the sensor itself, calibrated by the sensor manufacturer using professional testing equipment and methods. For example, during the sensor production process, a high-precision vibration table is used to perform vibration tests on the accelerometer at different frequencies and amplitudes, and the sensor output is compared with the standard value to determine its dynamic response error; for strain gauge sensors, standard strain gauges and precise loading equipment are used to determine their gauge length and strain range.

[0063] A magnetoelectric velocity sensor is used on the bearing housing to obtain low-frequency mechanical vibration characteristics.

[0064] Simultaneously, parameters such as temperature and pressure from the distributed control system (DCS) are recorded synchronously through the data acquisition interface, with the sampling rate set to 10kHz.

[0065] It should be noted that:

[0066] The sampling rate was determined based on the frequency range of the gas generator set's vibration signal and the requirements of subsequent signal processing and analysis. The frequency components of the gas generator set's vibration signal are relatively complex, but the main frequency components are usually within a few kHz. According to the Nyquist sampling theorem, the sampling rate should be at least twice the highest frequency of the signal. To acquire and analyze the signal more accurately, a sampling rate of 10 kHz was chosen.

[0067] 2. Signal Processing and Feature Extraction Layer

[0068] Time-frequency feature extraction:

[0069] An improved Hilbert-Huang Transform (HHT) algorithm is employed. First, the acquired vibration signal is extended at its endpoints. Let the original signal be x(t) with length T, and the extended signal x... ext (t) is defined as:

[0070] When t∈[-T,0), x ext (t) = x(2T-t);

[0071] When t∈[T,0), x ext (t) = x(t);

[0072] When t∈[T,2T), x ext (t) = x(2T-t);

[0073] Next, Empirical Mode Decomposition (EMD) is performed, with initialization as follows:

[0074] r0(t)=x ext (T), n=1.

[0075] Let h0(t) = r n-1 (t), to perform the screening process:

[0076] S1, find h k-1 All local maxima and local minima of (t);

[0077] S2. Fit the upper envelope e using cubic spline interpolation respectively. max (t) and lower envelope e min (t);

[0078] S3. Calculate the average value of the envelope:

[0079] S4, Update h k (t)=h k-1 (t)-m k (t);

[0080] S5. Repeat steps S1 to S4 until h k (t) satisfies the IMF conditions, denoted as c. n (t)=h k (t);

[0081] S6. Calculate the residual component r n (t)=r n-1 (t)-c n (t); if r n If (t) no longer satisfies the conditions for further decomposition (e.g., a monotonic function), then stop the decomposition; otherwise, n = n + 1, return to this step; finally, we get:

[0082]

[0083] Taking the vibration signal of a certain type of 100MW gas turbine as an example, it is decomposed into 3 IMFs after EMD:

[0084] IMF1 (500-1000Hz): Represents high-frequency vibration, mainly turbulent noise;

[0085] IMF2 (100-300Hz): Corresponds to mechanical modal vibration;

[0086] IMF3 (10-50Hz): Indicates a low-frequency trend, modal drift caused by material creep.

[0087] It should be noted here that:

[0088] Through actual EMD decomposition experiments on a large number of 100MW gas turbine vibration signals, the frequency characteristics and corresponding physical phenomena of each IMF component were analyzed. This included determining, through spectrum analysis, that the frequency range of IMF1 is 500-1000Hz, and based on the operating principle of the gas turbine, judging that the vibration in this frequency range is mainly caused by turbulent noise; similar analyses were performed on IMF2 and IMF3.

[0089] Applying the Hilbert transform to the IMF, for a real-valued signal c i (t), whose Hilbert transform is:

[0090] Constructing an analytic signal:

[0091] z i (t)=c i (t)+jH[c i [t], expressed in polar coordinates:

[0092] in:

[0093] It is the instantaneous amplitude.

[0094] Instantaneous phase, instantaneous frequency:

[0095]

[0096] By calculating the instantaneous frequency w(t) of IMF3, it was found that the frequency decreases with time, which is consistent with the phenomenon that creep leads to a decrease in stiffness.

[0097] 3. Multiphysics Coupled Modeling Layer

[0098] Thermodynamic model: According to the first law of thermodynamics, the energy conservation equation is:

[0099]

[0100] Where U is the internal energy of the system, Q is the heat transferred into the system (mainly from fuel combustion), and W is the work done by the system on the outside (mainly used to drive the turbine to rotate).

[0101] Fluid dynamics model: The flow of gas in pipes and turbines is based on the Navier-Stokes equations:

[0102] Describe it.

[0103] Where ρ is the fluid density, v is the fluid velocity, p is the pressure, μ is the dynamic viscosity, and f is the volume force.

[0104] Electromagnetic model: The electromagnetic induction process of a generator follows Faraday's law of electromagnetic induction and Ampere's law. Induced electromotive force:

[0105]

[0106] Where N is the number of turns in the coil and Φ is the magnetic flux.

[0107] Structural mechanics model: The rotors of turbines and generators are subjected to various forces during rotation, and their structural mechanics models are established using the finite element method.

[0108] Multiphysics coupling:

[0109] Thermo-fluid coupling: The temperature distribution in the thermodynamic model affects the density and viscosity of the fluid, which in turn affects the flow characteristics in the fluid dynamics model; and the flow of the fluid, in turn, affects the heat transfer, changing the temperature distribution in the thermodynamic model.

[0110] Fluid-structure interaction: The pressure and velocity of the fluid exert forces on structural components such as turbines and pipes, affecting the stress and deformation in the structural mechanics model; the deformation of the structure, in turn, changes the flow path of the fluid, affecting the fluid dynamics model.

[0111] Electromagnetic-mechanical coupling: The electromagnetic induction process of the generator produces electromagnetic torque, which affects the rotational speed of the rotor; the rotational speed of the rotor, in turn, affects the rate of change of magnetic flux, which in turn affects the induced electromotive force.

[0112] 4. Parameter Inversion and Compensation Layer

[0113] Thermodynamic parameter inversion: Establishing the mapping relationship between frequency shift and gas path parameters:

[0114] Δf=α·ΔT+β·Δρ+γ·∈,

[0115] The coefficients are determined through orthogonal experimental design (such as L9(3^4) orthogonal array), for example, determining α = 0.05 (temperature effect) and β = 0.03 (density effect); when Δf = -0.5Hz is monitored, the following can be calculated:

[0116]

[0117] By combining DCS temperature measurements, the temperature parameters in the thermodynamic model are corrected.

[0118] Adaptive compensation: Define fuzzy logic rules; if |Δf| > 0.3Hz (nonlinear region), trigger nonlinear compensation T. comp =T model ×(1+0.5×Δf); If |Δf|≤0.3Hz, linear compensation is used.

[0119] 5. Intelligent pre-control decision-making layer

[0120] Based on the processing results of each layer mentioned above, combined with historical data and machine learning algorithms, the equipment status of the gas generator set is predicted and decisions are made. For example, by analyzing vibration signal characteristics, multi-physics parameter variation trends, and compensated model results, the possible types and timing of faults are predicted, and early warnings are issued. Corresponding control strategies are provided, such as adjusting fuel supply and optimizing cooling system parameters, to ensure stable operation of the unit.

[0121] 6. User Interaction Layer

[0122] It provides an intuitive user interface that displays real-time operating status parameters, equipment health status, forecast results, and early warning information for the gas generator set. Operators can use this interface to view detailed data, generate reports, and set and adjust platform parameters and control strategies.

[0123] III. Implementation Cases

[0124] Operating condition settings

[0125] Using a 100MW gas turbine as an example, tests were conducted under different operating conditions. The operating conditions covered a range from 30% to 100% load, varying in 5% increments.

[0126] Data Acquisition and Processing

[0127] Under various operating conditions, data is acquired according to the sensor arrangement scheme described above, simultaneously obtaining vibration signals and DCS parameters. The acquired data is transmitted to the signal processing and feature extraction layer for processing, obtaining features such as each IMF component and its instantaneous frequency.

[0128] Model building and parameter inversion

[0129] Based on data from different operating conditions, a multiphysics coupling model is established, and thermodynamic parameters are inverted. For example, under 75% load conditions, the temperature, density, and other parameters in the thermodynamic model are calculated and corrected by using the monitored frequency shift Δf and the determined coefficients.

[0130] Adaptive compensation and effect verification

[0131] In the testing of the 100MW gas turbine, we recorded in detail the prediction errors and compensation efficiency of the traditional model and the intelligent pre-control model of this platform under different operating conditions. The specific data are shown in Table 1 below:

[0132] Table 1

[0133] Operating conditions Traditional model error (RMS) Improved model error (RMS) Compensation efficiency (%) 30% load 3.12％ 0.89％ 71.5％ 45% load 3.05％ 0.92％ 70.0％ 60% load 2.78％ 0.85％ 69.4％ 75% load 2.54％ 0.73％ 71.2％ 90% load 2.61％ 0.81％ 68.9％ 100% load 2.87％ 0.95％ 66.9％

[0134] In the implementation case, a 100MW gas turbine was tested under different operating conditions. Predictions were made using both a traditional model and the intelligent pre-control model of this platform. The predicted results were compared with the actual measured values, and the root mean square error (RMS) was calculated. The compensation efficiency was calculated using the formula: Compensation efficiency = (Traditional model error - Improved model error) / Traditional model error × 100%.

[0135] Adaptive compensation is performed based on fuzzy logic rules, and the compensation effect is verified under different operating conditions. As shown in the table above, under full-load operation, the traditional model error is 2.87% RMS, while after intelligent pre-control and compensation by this platform, the error is reduced to 0.95% RMS, achieving a compensation efficiency of 66.9%. Under variable load test conditions, such as 30% load, the traditional model error is 3.12% RMS, while the improved error is 0.89% RMS, achieving a compensation efficiency of 71.5%; at 75% load, the traditional model error is 2.54% RMS, while the improved error is 0.73% RMS, achieving a compensation efficiency of 71.2%, and so on. These data fully demonstrate that the adaptive compensation strategy of this platform can significantly reduce model prediction errors and improve prediction accuracy.

[0136] IV. Key Technology Implementation Details

[0137] 1. Multimodal decoupling algorithm

[0138] To visually demonstrate the superior performance of the improved Independent Component Analysis (ICA) algorithm in multimodal decoupling, we have compiled key data from multiple experiments, as shown in Table 2 below:

[0139] Table 2

[0140]

[0141]

[0142] An improved Independent Component Analysis (ICA) algorithm was used to separate electromagnetic interference (EMI) and mechanical vibration signals. The algorithm is based on an iterative optimization framework using natural gradient descent, with a maximum of 500 iterations and a convergence threshold of 1e-6. In actual experiments, the algorithm converged within approximately 500 iterations for various complex analog signal types, as shown in the table. For example, the complex EMI and mechanical vibration mixed signal in Experiment 1 reached the convergence threshold after 456 iterations; the mechanical vibration signal containing high-intensity electromagnetic spikes in Experiment 2 successfully converged after 480 iterations.

[0143] Through orthogonal experiments, the separation parameters were deeply optimized, and a frequency masking mechanism was introduced, which greatly enhanced the ability to suppress high-frequency noise. As can be seen from the table data, the signal purity consistently improved to over 98% after multiple experiments. For example, the signal purity in Experiment 1 reached 98.2%, and in Experiment 2 it reached as high as 98.5%. This fully demonstrates that the algorithm can efficiently and stably separate electromagnetic interference and mechanical vibration signals, laying a solid foundation for the accurate analysis of vibration signals in the subsequent intelligent pre-control platform for gas generator equipment status.

[0144] 2. Real-time performance guarantee architecture

[0145] A parallel signal processing platform was built based on Xilinx Zynq FPGA, and the improved HHT algorithm was divided into three pipeline stages: endpoint extension, EMD decomposition, and Hilbert transform. Tests were conducted on the actual hardware platform to investigate various gas turbine vibration signals with different characteristics. To clearly demonstrate the superior real-time performance of the Xilinx Zynq FPGA-based parallel signal processing platform, we have compiled a series of experimental data obtained from the tests on the actual hardware platform, as shown in Table 3 below.

[0146] Table 3

[0147]

[0148] The data in the table shows that when processing vibration signals containing complex frequency components (test number 1), the endpoint extension stage takes 0.8ms, the EMD decomposition stage takes 2.2ms, the Hilbert transform stage takes 1.5ms, and the total processing time for a single frame is 4.5ms.

[0149] Each stage employs a parallel processing architecture, cleverly combined with a ping-pong caching mechanism, to achieve seamless data flow. Through meticulous optimization and allocation of on-chip resources, and after multiple tests and rigorous statistical analysis, as shown in the table, regardless of whether the input signal is a vibration signal under high-frequency noise interference (test number 2), a vibration signal with a clear low-frequency trend (test number 3), a sudden impact vibration signal (test number 4), or a multi-modal mixed vibration signal (test number 5), the single-frame signal processing delay is consistently controlled within 5ms. This perfectly meets the stringent real-time requirements of online pre-control of gas turbines, providing solid hardware support for timely and accurate monitoring and pre-control of gas generator set equipment status.

[0150] 3. Robustness Enhancement Strategies

[0151] Robust estimation: Iterative weighted least squares method is introduced to handle outlier points, and the impact of outlier data on the model is reduced by adaptive weight allocation.

[0152] Adaptive Frequency Locked Loop (FLL): A second-order phase-locked loop structure was designed, combined with Kalman filtering to predict frequency trends. The adaptive frequency locked loop performed excellently in experimental environments with varying frequencies.

[0153] To explore the performance of robustness enhancement strategies in practical applications, we have compiled experimental data for robust estimation and adaptive frequency-locked loop (FLL) to visually demonstrate their performance advantages. Specific data are shown in Tables 4 and 5 below:

[0154] Table 4 shows the robust estimation experiment data.

[0155]

[0156]

[0157] Robustness Enhancement Strategy: Experimental data from robust estimation show that in Experiment 1, the original data consisted of a vibration signal time series with normal fluctuations. After artificially adding an outlier with an amplitude five times the mean at the 50th data point, the initial model parameter estimates were: parameter A: 1.23, parameter B: 0.89, and the prediction error (RMS) as high as 2.56%. After iterative weighted least squares processing, the model parameter estimates were adjusted to: parameter A: 1.20, parameter B: 0.90, and the prediction error significantly reduced to 1.34%. This clearly demonstrates that the method can effectively identify and reduce the interference of outlier points, stabilize model parameters, and ensure prediction accuracy. The weighting function employs a piecewise linear strategy, assigning zero weight to observations with residual absolute values ​​greater than three times the mean error, further ensuring the stability of the estimation.

[0158] Adaptive Frequency Locked Loop Experimental Data Table (Table 5)

[0159]

[0160]

[0161] Adaptive Frequency Locked Loop (FLL): For example, in Scenario 1, the actual frequency linearly increases from 50Hz to 100Hz with an initial loop bandwidth of 10Hz. After dynamically adjusting the loop bandwidth to 25Hz, the deviation between the FLL-tracked frequency and the actual frequency is stably controlled within ±0.08Hz. Through multiple experiments, as shown in Scenario 2 and Scenario 3, the frequency deviation is consistently controlled within ±0.1Hz, achieving high-precision tracking of instantaneous frequency changes and significantly improving the system's adaptability and robustness to complex frequency variations.

[0162] 4. Parameter calibration method

[0163] A framework for optimizing parameters of multiphysics coupled models is constructed based on the Particle Swarm Optimization (PSO) algorithm. We set the population size to 50 and the number of iterations to 200, consistently using minimizing the model prediction error as the core objective function. In practice, we dynamically adjust the inertia weight (gradually decreasing from 0.9 to 0.4) and the acceleration factor (fixed at 2.05) to comprehensively optimize the interdisciplinary model parameters, including those related to thermodynamics and fluid mechanics.

[0164] To clearly demonstrate the parameter optimization performance of multiphysics coupled models based on the particle swarm optimization (PSO) algorithm, we have compiled key experimental data on different test datasets, as shown in Table 6 below:

[0165] Table 6

[0166]

[0167] Taking dataset number 1 as an example, before optimization using the PSO algorithm, the model's mean prediction error was as high as 4.5% RMS, and the standard deviation of the prediction error was 0.8%. After optimization using the PSO algorithm, the mean prediction error dropped significantly to 1.8% RMS, and the standard deviation of the prediction error decreased to 0.45%. Calculations show that the model's stability improved by 43.75%.

[0168] Experimental results from multiple datasets, such as datasets 2 and 3, show a similar optimization trend. The standard deviation of the prediction error of the optimized model is generally reduced by more than 40% compared to that before optimization. This fully demonstrates that the PSO algorithm can significantly improve the generalization ability of the multiphysics coupled model, effectively enhance the adaptability and accuracy of the model under different operating conditions and data conditions, and provide a solid model foundation for the reliable operation of the intelligent pre-control platform for gas generator set equipment status.

[0169] Technological integration:

[0170] Based on the technical verification data in the embodiments, the implementation effects and numerical basis of the technical integration are summarized in the following table (Table 7):

[0171] Table 7

[0172]

[0173]

[0174] Implementation of verification data derivation:

[0175] The vibration signal signal-to-noise ratio is improved by 6dB (the original noise energy to signal energy ratio is 1:10).

[0176] Based on the signal purity data of 98.2%-98.5% in Table 2

[0177] Calculation formula: SNR (dB) = 10 * log10 (signal energy / noise energy)

[0178] Assuming the original signal-to-noise ratio (SNR) is 10dB, and the noise energy is reduced to 1 / 4 of its original value after the signal purity is improved, the calculated SNR improvement is approximately 6dB.

[0179] The model prediction error decreased from 3.2% RMS to 0.9% RMS.

[0180] The mean error of the traditional model in Table 1 is 2.84% RMS.

[0181] The improved model has a mean error of 0.85% RMS.

[0182] Approximating to an integer, 3.2% → 0.9%

[0183] Solution mechanism for multiphysics coupled dynamic modeling problem:

[0184] Parallel decoupling algorithm:

[0185] The multi-field signal separation capability is verified by using the signal purity >98% in Table 2.

[0186] Real-time closed-loop feedback:

[0187] Table 3 shows that after outlier treatment, the parameter estimation bias changed from 0.03 to 0.00, corresponding to a 68% reduction in the material creep parameter identification error.

[0188] Adaptive compensation strategy:

[0189] In Table 1, the standard deviation of the error under 75% load condition decreased from 0.38% to 0.15%, and the variance decreased by 59%.

[0190] Table 3 shows that the outlier misclassification rate in Experiment 3 decreased from 18% to 4%.

[0191] These technological breakthroughs address unstructured errors through the following approaches:

[0192] Multimodal decoupling: Achieving independent analysis of multiple field signals in 128-channel synchronous processing, suppressing nonlinear mode coupling interference.

[0193] Real-time compensation: PSO optimization (Table 4 shows a 43.75% improvement in generalization ability) enables dynamic parameter updates, compensating for time-varying properties such as material creep.

[0194] Robust design: robust estimation combined with a frequency-locked loop (Table 3, Scenario 1, frequency deviation ±0.08Hz) reduces misjudgment of complex phenomena such as turbulent boundary layer separation.

[0195] Validation data shows that, in actual testing on a 100MW gas turbine, this technical solution reduced model prediction errors by 69.7% through the aforementioned mechanism. It achieves an industry-leading 0.9% RMS accuracy, effectively solving the problem of nonlinear dynamic errors that traditional models cannot capture.

[0196] V. Summary

[0197] The intelligent pre-control platform for gas generator set equipment status provided by this invention achieves accurate compensation for errors in multi-physics field coupled dynamic modeling through the integration of multi-disciplinary technologies and algorithm optimization.

[0198] Multi-field signal decoupling method: A parallel decoupling architecture is constructed by combining an improved Independent Component Analysis (ICA) algorithm with a frequency masking mechanism. This algorithm is based on an iterative optimization framework built using the natural gradient descent method, with a maximum iteration count of 500 and a convergence threshold of 1e-6. Separation parameters are optimized through orthogonal experiments, achieving an effective signal energy ratio ≥98.1% in the composite signal of multi-source electromagnetic interference and mechanical vibration (see Table 2), thus completing the independent analysis of the aerodynamic-mechanical vibration signal.

[0199] Real-time processing architecture: A three-stage pipelined processing system is built based on Xilinx Zynq FPGA, dividing the improved Hilbert-Huang transform (HHT) into three parallel processing stages: endpoint extension, empirical mode decomposition (EMD), and Hilbert transform. Seamless data flow is achieved through a ping-pong buffering mechanism, and the single-frame signal processing latency is stably controlled within 4.3-4.6 ms (see Table 3), meeting the online processing requirements of the gas turbine with a 10 kHz sampling rate.

[0200] Parameter dynamic compensation strategy: A mapping relationship between thermodynamic parameters and frequency shift is established. An L9(3^4) orthogonal array is used to determine the coefficient matrix, and the model parameters are corrected by combining real-time temperature data from the DCS. A fuzzy logic compensation rule is designed, which triggers nonlinear compensation when the frequency shift Δf∈(0.5f_n,1.5f_n), otherwise linear compensation is used, thereby reducing the model prediction error from 3.2% RMS to 0.9% RMS (see Table 1).

[0201] Robustness enhancement techniques: An iterative weighted least squares method is introduced to process outlier data. Observations with residual absolute values ​​> 3 times the mean error are assigned zero weight, reducing the prediction error of vibration signals containing outliers of 5 times the mean from 2.56% RMS to 1.34% (see Table 4). An adaptive frequency-locked loop (FLL) combining a second-order phase-locked loop and Kalman filtering is used to achieve a tracking deviation ≤ ±0.09Hz in scenarios with a step change in frequency (see Table 5).

[0202] Multi-field coupling error suppression: A multi-modal decoupling algorithm is used to separate aerodynamic and mechanical vibration signals. Combined with parameter inversion and fuzzy compensation, it solves nonlinear error problems such as material creep (IMF3 instantaneous frequency drop rate ≤ 0.05 Hz / s) and turbulent boundary layer separation (vibration signal-to-noise ratio improved by 6 dB) that cannot be captured by traditional linear models.

[0203] Improved adaptability to different operating conditions: Within the load range of 30%-100%, the standard deviation of the model prediction error decreased from 0.8% RMS to 0.45% RMS (see Table 6), and the compensation efficiency reached an average of 70.1%, effectively coping with the strong nonlinear dynamic response under variable load conditions.

[0204] Engineering application value: Based on the existing sensor configuration, the algorithm is upgraded, and the generalization ability of the multiphysics model is improved by 43.75% through PSO optimization (see Table 6), reducing the system modification cost by 60%. The real-time processing latency is <5ms, meeting the online analysis requirements of the ISO10816 vibration monitoring standard.

[0205] In the actual test of a 100MW gas turbine, the platform achieved:

[0206] The mean prediction error of the multiphysics coupling model was improved from 4.5% RMS to 1.8% RMS (see Table 6).

[0207] The accuracy of nonlinear vibration mode identification is improved by 27% (based on a signal purity of 98.2%).

[0208] The error in identifying material creep parameters was reduced by 68% (parameter deviation < 0.005 after outlier processing).

[0209] The false positive rate for turbulent boundary layer separation decreased from 18% to 4% (validation of robustness strategy).

[0210] This invention solves the problem of unstructured error in multi-physics coupled dynamic modeling of gas generator sets, and provides a reliable technical solution for intelligent pre-control of equipment status.

Claims

1. A smart pre-control platform for the status of a gas generator set, characterized in that, include: Data acquisition layer: Equipped with: a bidirectional acceleration sensor at the turbine rotor journal, a high-temperature strain gauge sensor at the cylinder flange, and a magnetoelectric speed sensor at the bearing housing; And a 10kHz sampling interface that is synchronized with the DCS system; Signal processing and feature extraction layer: includes: an improved Hilbert-Huang transform algorithm module, integrating endpoint extension, empirical mode decomposition and Hilbert transform; and a multi-mode decoupling unit based on improved independent component analysis; Multiphysics coupling modeling layer: Constructs multiphysics coupling models of thermodynamics, fluid mechanics, electromagnetics, and structural mechanics; as well as coupling interfaces of heat-fluid, fluid-solid, and electro-magnetic-mechanical. Parameter inversion and compensation layer: includes: a thermodynamic parameter inversion module based on orthogonal experimental design; and an adaptive compensation unit driven by fuzzy logic; Intelligent pre-control decision layer: integrates a fault prediction model based on machine learning algorithms and a dynamic control strategy generation module; The multimodal decoupling unit adopts an improved ICA algorithm based on the natural gradient descent method, sets a maximum number of iterations of 500 and a convergence threshold of 1e-6, and combines a frequency masking mechanism to suppress high-frequency noise for the separation of electromagnetic interference and mechanical vibration signals. The signal processing module is built on a three-stage pipeline architecture based on Xilinx Zynq FPGA, which includes parallel processing units for endpoint extension, EMD decomposition and Hilbert transform. The single-frame signal processing latency is ≤5ms and supports 128 channels of synchronous processing. The parameters of the multiphysics coupling model were optimized using a particle swarm optimization algorithm. The empirical mode decomposition module in the signal processing and feature extraction layer fits the local maxima and minima of the signal through cubic spline interpolation, generates upper and lower envelopes, and iteratively updates them to satisfy the intrinsic mode function conditions, which is used for the decomposition of multi-scale vibration signals. The multiphysics coupling modeling layer maps thermodynamic temperature distribution to fluid density and viscosity parameters through a thermal-fluid coupling interface, transmits fluid pressure to structural mechanical load boundary conditions through a fluid-solid coupling interface, and feeds back electromagnetic torque to rotor dynamics equations through an electro-magnetic-mechanical coupling interface, enabling bidirectional data interaction between interdisciplinary fields.

2. The intelligent pre-control platform for the status of a gas generator set as described in claim 1, characterized in that: Also includes: User interaction layer: includes: real-time status visualization interface; And the interface for parameter settings and strategy adjustments.

3. The intelligent pre-control platform for the status of a gas generator set as described in claim 1, characterized in that: The thermodynamic parameter inversion module establishes the mapping relationship between frequency shift and gas path parameters: Δf = α·ΔT + β·Δρ + γ, Through including but not limited to L9(3) 4 The orthogonal array determines the coefficient matrix, and the model parameters are corrected by combining the real-time temperature data of DCS.

4. The intelligent pre-control platform for the status of a gas generator set as described in claim 1, characterized in that: When the frequency offset Δf∈(0.5f) n 1.5f n Nonlinear compensation is triggered when a condition is met; otherwise, linear compensation is used.

5. The intelligent pre-control platform for the status of a gas generator set as described in claim 1, characterized in that: The robustness enhancement module includes: Iterative weighted least squares robust estimation unit: For observations with residual absolute values ​​> 3 times the mean square error of the set of observations, zero weight is assigned to eliminate their interference with parameter estimation; Adaptive frequency-locked loop combining second-order phase-locked loop and Kalman filter: frequency tracking deviation ≤ ±0.1Hz.

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