Fault diagnosis method for main rotating part and bearing of industrial gas turbine

Abstract

The invention relates to a fault diagnosis method for a main rotating part and a bearing of an industrial gas turbine. The invention relates to the technical field of gas turbine fault diagnosis, and provides premise setting, engine performance and degradation model establishment, engine fault diagnosis and steady-state and transient model verification. According to the invention, faults of main rotating parts and bearings under dynamic conditions can be diagnosed at the same time, even under the condition of sudden failure; a continuous method for diagnosing faults of an industrial gas turbine using dynamic effect compensation under transient conditions is proposed; under steady-state and transient-state conditions, even under the condition of sudden failure, the health conditions of the three rotating parts and the bearing can be identified at the same time in real time with high precision.

Description

Technical Field

[0001] The present invention relates to the technical field of gas turbine fault diagnosis, and is a method for diagnosing faults of main rotating components and bearings of industrial gas turbines. Background Art

[0002] Since 1969, gas path analysis (GPA) has been a mature method for improving the reliability and safety of gas turbines. With the development of the new power system field, people combine gas turbines with renewable energy sources (such as wind energy and solar energy) to achieve carbon neutrality. However, renewable energy sources have characteristics such as intermittency, randomness, and volatility, which will affect the stability of the power grid. Therefore, the gas turbines in such systems will operate for a long time under transient conditions to achieve power grid control. Correspondingly, GPA under transient conditions, especially sudden failures during dynamic operation, has become the focus of attention in academia and industry.

[0003] Regarding the GPA of gas turbines operating under steady-state conditions, many scholars have conducted extensive research. In the past few years, research on analyzing the transient behavior of gas turbine engines using GPA has increased rapidly. However, few studies have attempted to identify sudden bearing failures through GPA. This is because previous studies have not systematically investigated the impact of bearing health on engine performance and fault diagnosis. In addition, the GPA of main rotating components and bearings has not been comprehensively studied under steady-state and transient conditions, especially the fault diagnosis of sudden bearing failures under dynamic conditions has not been studied. Inter-shaft bearing failures will reduce the mechanical efficiency of the engine shaft, not only affecting component matching, deteriorating engine performance, but also delaying the dynamic response of gas turbines.

[0004] Therefore, continuous fault diagnosis of main rotating components and bearings, especially diagnosing sudden failures under transient conditions, can effectively improve the reliability and safety of gas turbines. One of the main obstacles of GPA under transient conditions is the dynamic effect of the engine operating under unstable conditions. In this case, gas path measurements cannot be used alone for fault diagnosis, and the measurement results should be regarded as time series. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, to fill this gap in the literature and solve the dynamic effect of the engine operating under unstable conditions to achieve reliable and accurate gas turbine fault diagnosis; to explore the impact of bearing faults on performance and gas path analysis, and to evaluate the proposed method by assessing the dynamic impact on system operation to simultaneously identify engine component and bearing faults.

[0006] This invention studies the coupling relationship between engine bearing faults and gas path performance parameters. Aiming at the problem that the engine bearing fault diagnosis based on means such as vibration signals cannot directly reflect the degradation of engine performance parameters, a bearing fault diagnosis method based on gas path parameters is proposed. Combining with the hierarchical diagnosis method, it enriches the means of engine bearing fault diagnosis, and can quickly and accurately quantify and track the impact of bearing faults on performance parameters such as the engine safety margin and fuel consumption rate. This invention provides a fault diagnosis method for the main rotating components and bearings of industrial gas turbines.

[0007] This invention provides the following technical solutions:

[0008] A fault diagnosis method for the main rotating components and bearings of industrial gas turbines, the method comprising the following steps:

[0009] Step 1: Put forward premise settings;

[0010] Step 2: Establish an engine performance and degradation model;

[0011] Step 3: Conduct engine fault diagnosis;

[0012] Step 4: Carry out steady-state and transient model verification.

[0013] Preferably, the specific content of step 1 is:

[0014] The fault level of the rotating component is defined by the degradation factors of efficiency, flow rate and pressure ratio, and the relative deviations of the flow rate and pressure ratio are set to be the same;

[0015] The bearing fault level is represented according to the mechanical efficiency degradation factor;

[0016] The health conditions of both the rotating component and the bearing will gradually deteriorate over time, and the gradual faults can follow an exponential trend; sudden faults can also occur simultaneously in all concerned components under dynamic conditions to test the robustness of the proposed method.

[0017] Preferably, the specific content of step 2 is:

[0018] The engine includes a high-pressure compressor, a combustor, a high-pressure turbine and a power turbine. The high-pressure and low-pressure shafts are respectively supported by high-pressure and low-pressure bearings. A hydrogen-fueled twin-shaft engine model is constructed using the components of TurboWorkX;

[0019] The degradation factor X of each performance characteristic of any specific component is defined by formula 1 to quantify the deviation between the degradation state and the healthy state:

[0020]

[0021] The fault propagation is determined by Equation 2, which represents the progressive development of the EFLH component fault:

[0022] |ΔX|·X = |PC d -PC h | = 1 - exp(m·EFLH n ) (2).

[0023] Preferably, the lag response of the speed sensor is determined:

[0024] The negative impact of the lag response is addressed by the first-order lag theory defined by Equation 3:

[0025]

[0026] where τ is the time constant, N in (s) represents the actual shaft speed, N out (s) represents the shaft speed with delay measured by the sensor.

[0027] Preferably, the lag response of the rotor dynamics is determined:

[0028] Under dynamic conditions, the acceleration and deceleration of the shaft are represented by Equation 4, which is related to the acceleration of the shaft speed N and the moment of inertia I of the shaft:

[0029]

[0030] SP is obtained by moving and changing terms according to Equation 5:

[0031]

[0032] Obtained from Equation 6:

[0033]

[0034] where Δt is the time step between two adjacent moments;

[0035] The same power equation for the HP shaft is determined by Equation 7:

[0036] TW HPT =SP HP + CW + EL (7)

[0037] where TW HPT is the work done by the HP turbine, CW is the work required to compress air in the coaxial compressor, and EL is the external load through power output;

[0038] For the LP shaft, the same power equation is found in Equation 8:

[0039] TW LPT = SP LP + PS (8)

[0040] Wherein, TW LPT is the work done by the low-pressure turbine, and PS is the power setting in the coaxial generator.

[0041] Preferably, the hysteresis response of the temperature is determined:

[0042] When the engine operates at a high power setting for a period of time, suddenly reduces the power and accelerates back to the high power setting, compared with normal acceleration, the transient operating line of the compressor is closer to the surge boundary, and the hot soak increases the average and maximum gas path analysis errors by 1.1% and 7% respectively, and is accompanied by a sharp acceleration.

[0043] Preferably, step 3 is specifically:

[0044] The relationship between the degradation factor X, the power setting parameter Y, and the engine measurement value Z is defined by Equation 10:

[0045] Z = ICM(X,Y) (10)

[0046] Wherein, ICM is the influence coefficient matrix, and X includes the high-pressure compressor efficiency factor X HPC,E , the high-pressure compressor flow factor X HPC,F , the high-pressure turbine efficiency factor X HPT,E , the high-pressure turbine flow factor X HPT,F , the low-pressure turbine efficiency factor X LPT,E , the low-pressure turbine flow factor X LPT,F , the high-pressure shaft mechanical efficiency factor X HPSB , the low-pressure shaft mechanical efficiency factor X LPSB .

[0047] The root mean square error RMSE formula 11 is selected to evaluate the convergence of the fault diagnosis within the preset threshold of 1E-5:

[0048]

[0049] Wherein, Z P,i is the predicted parameter of the i-th measurement, Z A,i is the i-th measurement of the actual parameter, and k is the total number of measurements;

[0050] The relative error RE of the diagnosis is defined by Equation 12:

[0051]

[0052] Wherein, X P,j is the predicted value of the j-th degradation factor, X A,jis the actual value of the j-th degradation factor;

[0053] By calculating the intake port model, the temperature and pressure at the compressor intake port are obtained, and the total pressure at the compressor outlet is measured to obtain the compressor pressure ratio.

[0054] The intake conditions and fuel flow rate are determined, the outlet parameters of the burner are calculated, and the inlet parameters of the HPT are obtained through the cooling mixing model; at the HPT, through the measured P 45 Obtain the pressure drop ratio;

[0055] Similar to the HPC, the outlet parameters of the HPT are established. The calculation methods of the mixing model and the LPT model are the same as those of the first mixing model and the HPT model respectively. After calculating the pressure loss at the LPT outlet through the pipeline model, the air flow finally expands into the atmosphere.

[0056] Eight selected convergence criteria, including power balance, nozzle area, air flow consistency, and gas path temperature. Under transient conditions, the rotor and temperature will change dynamically. Due to the consideration of the dynamic compensation of the moment of inertia and the heat soak effect, it is possible to accurately track the sudden failures of rotating components and bearings during the dynamic process with high precision, so as to accurately monitor the health status.

[0057] An industrial gas turbine main rotating component and bearing fault diagnosis system, the system includes:

[0058] A setting module, the setting module proposes premise settings;

[0059] A model establishment module, the model establishment module establishes an engine performance and degradation model;

[0060] A fault diagnosis module, the fault diagnosis module performs engine fault diagnosis;

[0061] A verification module, the verification module conducts steady-state and transient model verification.

[0062] A computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to be used to implement an industrial gas turbine main rotating component and bearing fault diagnosis method.

[0063] A computer device, including a memory and a processor, the memory stores a computer program, and the processor implements an industrial gas turbine main rotating component and bearing fault diagnosis method when executing the computer program.

[0064] The present invention has the following beneficial effects:

[0065] Compared with the prior art, the present invention:

[0066] The present invention proposes a GPA method that can diagnose faults of main rotating components and bearings under dynamic conditions simultaneously, even in the case of their sudden failure; a continuous method for diagnosing faults of industrial gas turbines using dynamic effect compensation under transient conditions; and under steady-state and transient conditions, even in the case of sudden failure, the health conditions of three rotating components and bearings can be identified simultaneously with high precision in real time.

[0067] The present invention provides innovative viewpoints on the influence of bearing faults on engine performance and fault diagnosis, and at the same time provides important insights for the implementation of hydrogen fuel gas turbine power generation.

[0068] The present invention takes into account three dynamic effects: the lag response of speed sensors, rotor dynamics, and thermal soaking.

[0069] The present invention enriches the means of engine bearing fault diagnosis and can quickly and accurately quantify and track the influence of bearing faults on performance parameters such as the engine safety margin and fuel consumption rate.

[0070] The present invention provides new insights into the application of GPA in industrial gas turbines, that is, it is possible to improve the reliability of the engine through condition-based maintenance.

[0071] The present invention can quantitatively evaluate the influence of bearing failure on engine performance indicators and GPA results. Existing statistical data emphasizes the importance of including bearing faults in gas path calculations.

[0072] The method proposed by the present invention will be empirically compared with the state-of-the-art methods to demonstrate the significant benefits of using dynamic effect compensation. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0074] Figure 1 Shown is a sectional view of the engine of the present invention;

[0075] Figure 2 Shown is a process diagram of the fault diagnosis of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0077] The present invention has been described in detail below in conjunction with specific embodiments. Specific Embodiment 1:

[0079] According to Figures 1 to 2 as shown, the specific optimized technical solution adopted by the present invention to solve the above technical problems is: the present invention relates to a method for diagnosing faults of main rotating components and bearings of an industrial gas turbine.

[0080] A method for diagnosing faults of main rotating components and bearings of an industrial gas turbine, the method comprising the following steps:

[0081] Step 1: Propose a premise setting;

[0082] Step 2: Establish an engine performance and degradation model;

[0083] Step 3: Conduct engine fault diagnosis;

[0084] Step 4: Carry out steady-state and transient model verification. Specific Embodiment 2:

[0086] The difference between Embodiment 2 and Embodiment 1 of the present application is only that:

[0087] Specifically, Step 1 is:

[0088] The fault level of the rotating component is defined by the degradation factors of efficiency, flow rate, and pressure ratio, and the relative deviations of the flow rate and pressure ratio are set to be the same;

[0089] The bearing fault level is represented according to the mechanical efficiency degradation factor;

[0090] The health conditions of both the rotating component and the bearing will gradually deteriorate over time, and the gradual fault can follow an exponential trend; sudden faults can also occur simultaneously for all the components concerned under dynamic conditions to test the robustness of the proposed method. Specific Embodiment 3:

[0092] The difference between Embodiment 3 and Embodiment 2 of the present application is only that:

[0093] Specifically, Step 2 is:

[0094] The engine includes a high-pressure compressor, a combustor, a high-pressure turbine, and a power turbine. The high-pressure and low-pressure shafts are supported by high-pressure and low-pressure bearings respectively. A two-shaft engine model fueled by hydrogen is constructed using components of TurboWorkX;

[0095] The degradation factor X of each performance characteristic of any specific component is defined by Formula 1 to quantify the deviation of the degradation state from the healthy state:

[0096]

[0097] The fault propagation is determined by Formula 2, and the fault propagation represents the gradual development of the EFLH component fault:

[0098] |ΔX|·X = |PC d -PC h | = 1 - exp(m·EFLH n ) (2). Specific Embodiment Four:

[0100] The difference between Embodiment Four and Embodiment Three of this application lies only in:

[0101] Determine the hysteretic response of the speed sensor:

[0102] The negative impact of the hysteretic response is solved by the first-order hysteresis theory defined by Formula 3:

[0103]

[0104] where τ is the time constant, N in (s) represents the actual shaft speed, N out (s) represents the shaft speed with delay measured by the sensor. Specific Embodiment Five:

[0106] The difference between Embodiment Five and Embodiment Four of this invention lies only in:

[0107] Determine the hysteretic response of the rotor dynamics:

[0108] Under dynamic conditions, the acceleration and deceleration of the shaft are represented by Formula 4, which is related to the acceleration of the shaft the shaft speed N and the moment of inertia I of the shaft:

[0109]

[0110] Move and change the terms according to Formula 5 to obtain SP:

[0111]

[0112] Obtained from Formula 6:

[0113]

[0114] Among them, Δt is the time step between two adjacent moments;

[0115] The same power equation for the HP shaft is determined by Equation 7:

[0116] TW HPT = SP HP + CW + EL (7)

[0117] Among them, TW HPT is the work done by the HP turbine, CW is the work required to compress air in the coaxial compressor, and EL is the external load through power output;

[0118] Regarding the LP shaft, the same power equation is found in Equation 8:

[0119] TW LPT = SP LP + PS (8)

[0120] Among them, TW LPT is the work done by the low-pressure turbine, and PS is the power setting in the coaxial generator. Specific Embodiment Six:

[0122] The difference between Embodiment Six and Embodiment Five of the present invention is only that:

[0123] Determine the lag response of the temperature:

[0124] When the engine runs at a high power setting for a period of time, suddenly reduces the power and accelerates back to the high power setting, compared with normal acceleration, the transient operating line of the compressor is closer to the surge boundary, and the hot soak increases the average and maximum gas path analysis errors by 1.1% and 7% respectively, and is accompanied by a violent acceleration. Specific Embodiment Seven:

[0126] The difference between Embodiment Seven and Embodiment Six of the present invention is only that:

[0127] The specific step 3 is as follows:

[0128] The relationship between the degradation factor X, the power setting parameter Y, and the engine measurement value Z is defined by Equation 10:

[0129] Z = ICM(X, Y) (10)

[0130] Among them, ICM is the influence coefficient matrix, and X includes the high-pressure compressor efficiency factor X HPC,E , the high-pressure compressor flow factor X HPC,F , the high-pressure turbine efficiency factor X HPT,E , the high-pressure turbine flow factor XHPT,F , the low-pressure turbine efficiency factor X LPT,E , the low-pressure turbine flow factor X LPT,F , the high-pressure shaft mechanical efficiency factor X HPSB , the low-pressure shaft mechanical efficiency factor X LPSB .

[0131] Select the root mean square error RMSE formula 11 to evaluate the convergence of the fault diagnosis within the preset threshold of 1E-5:

[0132]

[0133] where Z P,i is the predicted parameter of the i-th measurement, Z A,i is the i-th measurement of the actual parameter, and k is the total number of measurements;

[0134] The relative error RE of the diagnosis is defined by formula 12:

[0135]

[0136] where X P,j is the predicted value of the j-th degradation factor, and X A,j is the actual value of the j-th degradation factor;

[0137] By calculating the inlet port model, the temperature and pressure at the compressor inlet port are obtained, and the total pressure at the compressor outlet is measured to obtain the compressor pressure ratio;

[0138] The inlet conditions and fuel flow rate are determined, the outlet parameters of the burner are calculated, and the inlet parameters of the HPT are obtained through the cooling mixing model; at the HPT, the pressure drop ratio is obtained by measuring P 45 ;

[0139] Similar to the HPC, the outlet parameters of the HPT are established. The calculation methods of the mixing model and the LPT model are the same as those of the first mixing model and the HPT model respectively. After calculating the pressure loss at the LPT outlet through the pipeline model, the air flow finally expands into the atmosphere;

[0140] The eight selected convergence criteria, including power balance, nozzle area, air flow consistency, and gas path temperature, the rotor and temperature will change dynamically under transient conditions. Due to the dynamic compensation considering the moment of inertia and heat soak effect, it can accurately quantify and track the sudden faults of rotating components and bearings during the dynamic process, so as to accurately monitor the health status. Specific embodiment eight:

[0142] The difference between the eighth embodiment of the present invention and the seventh embodiment is only that:

[0143] The present invention provides a fault diagnosis system for the main rotating components and bearings of an industrial gas turbine, and the system includes:

[0144] A setting module, which proposes premise settings;

[0145] A model establishment module, which establishes an engine performance and degradation model;

[0146] A fault diagnosis module, which conducts engine fault diagnosis;

[0147] A verification module, which conducts steady-state and transient model verification. Specific Embodiment Nine:

[0149] The difference between Embodiment Nine and Embodiment Eight of the present invention is only that:

[0150] The present invention provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to implement a method for diagnosing faults of the main rotating components and bearings of an industrial gas turbine. Specific Embodiment Ten:

[0152] The difference between Embodiment Ten and Embodiment Nine of the present invention is only that:

[0153] The present invention provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, a method for diagnosing faults of the main rotating components and bearings of an industrial gas turbine is implemented. Specific Embodiment Eleven:

[0155] The difference between Embodiment Eleven and Embodiment Ten of the present invention is only that:

[0156] The present invention aims to make up for this gap in the literature and solve the dynamic effects of the engine working under unstable conditions to achieve reliable and accurate gas turbine fault diagnosis; explore the influence of bearing faults on performance and gas path analysis, and evaluate the proposed method by evaluating the dynamic influence on system operation to simultaneously identify engine component and bearing faults.

[0157] The present invention studies the coupling relationship between engine bearing faults and gas path performance parameters. Aiming at the problem that the engine bearing fault diagnosis based on means such as vibration signals cannot directly reflect the degradation of engine performance parameters, a bearing fault diagnosis method based on gas path parameters is proposed, combined with a hierarchical diagnosis method, which enriches the means of engine bearing fault diagnosis and can quickly and accurately quantify and track the influence of bearing faults on performance parameters such as the engine safety margin and fuel consumption rate.

[0158] Method for diagnosing and identifying main rotating components and bearings of a gas turbine of the present invention, wherein the faults include steady-state faults and transient faults, and the method steps include:

[0159] Put forward a premise hypothesis;

[0160] Establish an engine performance and degradation model;

[0161] Conduct engine fault diagnosis;

[0162] Carry out steady-state and transient model verification;

[0163] Select different cases for analysis.

[0164] I. Put forward hypotheses

[0165] When verifying the effectiveness and ability of the method used, the present invention makes several assumptions.

[0166] 1. The present invention does not cover sensor-related problems to conduct a fair and clear evaluation of the diagnosis.

[0167] 2. The efficiency, flow rate, and pressure ratio degradation factors define the fault levels of the main rotating components. It is assumed that the relative deviations of the flow rate and pressure ratio are the same.

[0168] 3. The mechanical efficiency degradation factor represents the fault level of the shaft bearing.

[0169] 4. All main rotating components and bearings will deteriorate over time, and these faults follow an exponential law. In addition, all components of concern may suddenly fail simultaneously under dynamic conditions to test the robustness of the proposed method.

[0170] II. Establish an engine model.

[0171] The engine configuration is as Figure 1 shown. It includes a high-pressure compressor, a combustor, a high-pressure turbine, and then a low-pressure turbine coaxial with the generator. The high-pressure and low-pressure shafts are respectively supported by high-pressure bearings and low-pressure bearings. The available gas path measurement parameters of the engine are shown in Table 1.

[0172] Table 1 Gas path parameters of the engine

[0173]

[0174]

[0175] According to the above engine configuration, a hydrogen-fueled twin-shaft engine model is constructed using the components of TurboWorkX.

[0176] III. Introduce the influence of degradation

[0177] Based on the model, the degradation of the main rotating components and bearings is considered simultaneously. The degradation factor (X) of each performance characteristic of any specific component can be defined by Equation (1) to quantify the deviation of the degradation state from the healthy state.

[0178]

[0179] Table 2 summarizes the degradation factors and health conditions related to gas turbines. "Health state I" refers to a two-shaft industrial gas turbine that has completed 25,000 equivalent full-load hours (EFLH). For "Health state II", the degree of degradation is twice that of "Health state I". The transition from "Health state I" to "Health state II" represents an engine sudden failure.

[0180] Table 2 Degradation factors of a given gas turbine

[0181]

[0182]

[0183] IV. Increase fault propagation.

[0184] Assume that "Health state I" in Table 2 corresponds to 25,000 equivalent full-load hours (EFLH), which means that an overhaul of the gas turbine is required. Fault propagation is defined by Equation (2), which represents the gradual development of component failures in EFLH.

[0185] |ΔX|·X = |PC d -PC h | = 1 - exp(m·EFLH n ) (2)

[0186] where m is the attenuation rate of the degradation factor and n is the exponential time scaling factor.

[0187] V. Increase dynamic effect compensation.

[0188] When the engine operates under transient conditions, none of its components are in a thermodynamic equilibrium state. Therefore, the gas path measurements carried out independently at each time point cannot be used for gas path analysis (GPA) and cannot provide reliable feedback on the engine health condition. In this case, it is necessary to incorporate dynamic effects so that the diagnostic system can perform fault diagnosis at every moment, especially under transient conditions.

[0189] This study considered three typical dynamic effects: the lag response of the speed sensor, rotor dynamics, and thermal soaking.

[0190] Lag response of the speed sensor:

[0191] Under transient conditions, the shaft speed measured by the sensor may lag behind the actual speed. If the speed used in the engine model is not the actual speed, incorrect shaft speed values will be used to read the compressor and turbine characteristic maps. The negative impact of the lag response is addressed by the first-order lag theory defined by Equation (3).

[0192]

[0193] where τ is the time constant, N in (s) represents the actual shaft speed, N out (s) represents the shaft speed with delay measured by the sensor.

[0194] Rotor dynamics:

[0195] The expansion work in the turbine is not equal to the power required during transient maneuvers. To meet the power demand of each shaft, the surplus power (SP) of each shaft must be quantified and the shaft power identification equation must be reformulated. Under dynamic conditions, the acceleration and deceleration of the shaft can be represented by Equation (4), which is related to the acceleration of the shaft speed (N) and the moment of inertia (I) of the shaft.

[0196]

[0197] Then, SP can be obtained by moving and changing terms according to Equation (5) as follows. Obviously, when, is available, the SP at each moment can be found. Under steady-state conditions, this will be equal to zero because the shaft acceleration is zero.

[0198]

[0199] can be obtained from Equation (6), which is a more advanced control parameter compared to traditional gas turbine speed control,

[0200]

[0201] where Δt is the time step between two adjacent moments.

[0202] The same power equation for the HP shaft can be determined by Equation (7). It should be noted that the equality of the two equations is established for both steady-state and transient states,

[0203] TW HPT = SP HP + CW + EL (7)

[0204] where, TW HPTThe work done by the HP turbine is W, the work required to compress air in the coaxial compressor is CW, and the external load through power output is EL.

[0205] Regarding the LP shaft, the same power equation can be found in Equation (8).

[0206] TW LPT = SP LP + PS (8)

[0207] Where TW LPT is the work of the low-pressure turbine, and PS is the power setting in the coaxial generator.

[0208] Hot soak:

[0209] Under steady-state conditions, the metal and gas temperatures will be in thermal equilibrium. However, the thermal inertia of the metal in the engine will delay the thermal equilibrium under dynamic conditions, especially during the reslam maneuver. The transient operating line of the compressor under hot reslam: This occurs when the engine operates at a high power setting for a period of time, then suddenly reduces power and accelerates back to the high power setting. It should be noted that the transient operating line of the compressor during reslam is closer to the surge margin compared to normal acceleration. Unconventional conditions must be closely monitored. This is because during transient maneuvers and new power cycles, the gas turbine needs to operate in a way that maintains grid stability. Data shows that due to hot soak, the exhaust gas temperature of the engine will be significantly delayed. Therefore, if hot soak is ignored, the relative errors of the exhaust gas temperature and other gas path measurements will propagate to the diagnostic results. It should be noted that hot soak will increase the average and maximum gas path analysis errors by 1.1% and 7% respectively, along with a sharp acceleration.

[0210] The fault diagnosis method proposed by the invention takes into account hot soak under various operating conditions. Hot soak is calculated using Equation (9), which includes a delay in exponential form.

[0211] Q = A·U·(T g - T m )·(e -Δt / τ - 1) (9)

[0212] Where Q is the heat transfer between the gas and the metal, A is the heat transfer area, U represents the heat transfer coefficient, T g represents the gas temperature, T m is the engine metal temperature, and τ is the time constant.

[0213] VI. Conduct engine fault diagnosis.

[0214] Although the health condition of the engine cannot be directly measured, the effects of deterioration of each component are also reflected in the measured parameters. The relationship between the deterioration factor (X), the power setting parameter (Y), and the engine measurement value (Z) can be defined by Equation (10),

[0215]

[0216] where ICM is the influence coefficient matrix.

[0217] The value of X can be obtained through an iterative solver to minimize the difference between the predicted measurement value and the actual measurement value. In this study, the Newton - Raphson method was used for engine performance simulation and GPA.

[0218] The root mean square error (RMSE) Equation (11) was selected to evaluate the convergence of the fault diagnosis within a preset threshold (1E - 5).

[0219]

[0220] where, Z P,i is the predicted parameter for the i - th measurement, Z A,i is the i - th measurement of the actual parameter, and k is the total number of measurements.

[0221] The relative error (RE) of the diagnosis is defined by Equation (12):

[0222]

[0223] where, X P,j is the predicted value of the j - th degradation factor, X A,j is the actual value of the j - th degradation factor.

[0224] The fault diagnosis process is as Figure 2 shown. The fuel flow rate is a control parameter of the gas turbine engine. Table 2 summarizes the important deterioration factors, which are marked in purple in Figure 2 . The direct gas path measurements, such as the ambient temperature (T1) and pressure (P1) and the indirect gas path measurements, such as the surplus power (SP) and the metal temperature at the combustor outlet (T 4,M ), are shown in green blocks. The red block contains the gas turbine model. The iteration will go through an update process consistent with the compatibility verification, as shown in the blue part. The detailed steps are described as follows.

[0225] The total inlet parameters are known. By calculating the inlet port model, the temperature and pressure at the compressor inlet can be obtained. The total pressure at the compressor outlet can be measured, thereby obtaining the compressor pressure ratio. Given that the inlet conditions of the HPC and its characteristic map are already known, as well as the shaft speed and pressure ratio of the HPC, the outlet temperature, pressure, and the work done by the HPC can be determined.

[0226] Since the inlet conditions and fuel flow rate have been determined, the outlet parameters of the combustor can be calculated. Subsequently, the inlet parameters of the HPT can be obtained through the cooling mixture calculation. At the HPT, the pressure drop ratio is obtained by measuring P 45 and P4, which is determined by the cooling mixture calculation. Similar to the HPC, the outlet parameters of the HPT are established. The calculation methods of the following mixing model and LPT model are the same as those of the first mixing model and HPT model respectively. After calculating the pressure loss at the LPT outlet through the pipeline model, the air flow finally expands into the atmosphere.

[0227] Eight convergence criteria are selected, as shown by the blue blocks. These criteria include power balance, nozzle area, consistency of air flow, and gas path temperature, especially T3, T 45 , T5. It should be emphasized that under transient conditions, the rotor and temperature dynamics will change. Therefore, compensation for dynamic effects is incorporated to enable the monitoring of the health status.

[0228] VII. Conduct model verification.

[0229] The cross-section of the relevant industrial gas turbine is as Figure 1 shown, similar to the GE LM2500+. The commercial software GasTurb is selected to verify the proposed engine model using hydrogen fuel for transient and steady-state operations, and the benchmark is set as the reference engine in the following section. Table 3 shows the design point specifications of the given twin-shaft engine.

[0230] Table 3

[0231] Operating condition Unit Value Engine inlet pressure Standard atmospheric pressure (atm) 1 Engine inlet temperature Kelvin (K) 288.15 Ambient relative humidity ％ 60 Intake air mass flow rate Kilogram per second (kg / s) 80 Fuel flow rate Kilogram per second (kg / s) 0.6560 Free turbine work output Megawatt (MW) 31.98

[0232] Model verification under steady-state conditions:

[0233] The engine needs to adapt to the demand fluctuations of the national power grid not only at its design point but also at off-design points. Therefore, it is crucial to verify the performance model of the engine under design and off-design point conditions. Model verification under steady-state conditions requires comparing the results of GasTurb and the developed model under design point conditions with the Figure 1 model shown in, and recording the relative peak errors of each part.

[0234] The next stage is to verify the off-design conditions, where the developed model and GasTurb use the same composition map, and both models use the "engine power" setting as the main command condition. Here, for verification, the setting progresses from 60% to 100% in 5% steps. The results of the two models are compared to verify whether the developed gas turbine model is sufficient to simulate the design and off-design conditions. Thus, it is verified whether the proposed GPA algorithm can be implemented together with the developed model to identify the faults of the main rotating components and bearings.

[0235] Model verification under transient conditions:

[0236] During the 15-second acceleration operation, the power setting rises from 85% to 100%. After that, a 15-second deceleration will start, during which the power setting is reduced from 100% to 85%. The fuel plan for these power settings at 30-second intervals is obtained from GasTurb, and then the transient behavior is simulated with the developed model and compared and verified with GasTurb.

[0237] VIII. Select cases for analysis.

[0238] This step will select five cases to study the effects of bearing faults on engine performance and gas path analysis under different conditions. The purposes of the five cases are summarized as follows:

[0239] Case 1: The effect of bearing faults on engine performance;

[0240] Case 2: The effect of bearing faults on gas path analysis under steady-state conditions;

[0241] Case 3: Gas path analysis under steady-state conditions;

[0242] Case 4: Gas path analysis with a constant healthy state during transient operation;

[0243] Case 5: Gas path analysis with a sudden failure during transient maneuvering.

[0244] Through the above cases, the effects of bearing degradation on the performance of gas turbines and on gas path measurements can be explored.

[0245] The above description is only a preferred implementation of a method for diagnosing faults of the main rotating components and bearings of an industrial gas turbine. The protection scope of a method for diagnosing faults of the main rotating components and bearings of an industrial gas turbine is not limited to the above embodiments. All technical solutions within the scope of this concept belong to the protection scope of the present invention. It should be noted that for those skilled in the art, several improvements and changes made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. A method for diagnosing faults of main rotating parts and bearings of an industrial gas turbine, characterized in that: The method comprises the following steps: Step 1: Propose a premise; Step 2: Build engine performance and degradation model; Step 3: Perform engine fault diagnosis; Step 4: Conduct steady-state and transient model validation.

2. The method according to claim 1, characterized in that: The step 1 is specifically as follows: The failure level of the rotating components is defined by the efficiency, flow and pressure ratio degradation factors, assuming the same relative deviations of flow and pressure ratio; Representing the bearing failure level according to the mechanical efficiency degradation factor; The health of rotating parts and bearings can deteriorate over time, and gradual failures can evolve following an exponential trend; all components of interest can also fail suddenly simultaneously under dynamic conditions to test the robustness of the proposed approach.

3. The method according to claim 2, characterized in that: The step 2 is specifically as follows: The engine consists of a high-pressure compressor, a burner, a high-pressure turbine and a power turbine. The high-pressure and low-pressure shafts are supported by high-pressure bearings and low-pressure bearings respectively. Use TurboWorkX components to build a dual-shaft engine model using hydrogen as fuel. The degradation factor X for each performance characteristic of any particular component is defined using Equation 1 to quantify the deviation of the degraded state from the healthy state: The fault propagation, which represents the gradual development of the EFLH component failure, is determined by Equation 2: |ΔX|·X=|PC d -PC h |=1-exp(m·EFLH n ) (2)。 4. The method according to claim 1, characterized in that: Determine the hysteresis response of the speed sensor: The negative impact of the delayed response is addressed by the first-order hysteresis theory defined in Equation 3: Where τ is the time constant, N in (s) represents the actual shaft speed, N out (s) represents the axis speed with delay measured by the sensor.

5. The method according to claim 4, characterized in that: Determine the hysteretic response of the rotor dynamics: Under dynamic conditions, the acceleration and deceleration of the axis is expressed by formula 4, which is related to the acceleration of the axis The shaft speed N is related to the shaft's moment of inertia I: SP is obtained by moving and changing terms according to Formula 5: N is obtained from Formula 6: Among them, Δt is the time step between two adjacent moments; The same power equation for the HP shaft is determined by Equation 7: TW HPT =SP HP +CW+EL (7) Among them, TW HPT is the work done by the HP turbine, CW is the work required to compress the air in the coaxial compressor, and EL is the external load through the power output; With respect to the LP axis, the same power equation is found in Equation 8: TW LPT =SP LP +PS (8) Among them, TW LPT is the work done by the low-pressure turbine and PS is the power setting in the coaxial generator.

6. The method according to claim 5, characterized in that: Determine the hysteresis response of temperature: When the engine runs at a high power setting for a period of time, suddenly reduces the power and accelerates back to the high power setting, the transient operating line of the compressor is closer to the surge boundary compared with normal acceleration, and hot soak increases the average and maximum gas path analysis errors by 1.1% and 7%, respectively, accompanied by violent acceleration.

7. The method according to claim 6, characterized in that: The step 3 is specifically as follows: The relationship between the degradation factor X, the power setting parameter Y and the engine measurement value Z is defined by Equation 10: Z=ICM(X,Y) (10) Where ICM is the influence coefficient matrix, X includes the high pressure compressor efficiency factor X HPC,E , high pressure compressor flow factor X HPC,F , high pressure turbine efficiency factor X HPT,E , high pressure turbine flow factor X HPT,F , low pressure turbine efficiency factor X LPT,E , low pressure turbine flow factor X LPT,F , high pressure shaft mechanical efficiency factor X HPSB , low pressure shaft mechanical efficiency factor X LPSB ; The root mean square error RMSE formula 11 is selected to evaluate the convergence of fault diagnosis within the preset threshold 1E-5: Among them, Z P,i is the prediction parameter of the ith measurement, Z A,i is the ith measurement of the actual parameter, k is the total number of measurements; The relative error RE of the diagnosis is defined by Equation 12: Among them, X P,j is the predicted value of the jth degradation factor, X A,j is the actual value of the jth degradation factor; By calculating the air inlet model, the temperature and pressure at the compressor inlet are obtained, and the total pressure at the compressor outlet is measured to obtain the compressor pressure ratio; The intake conditions and fuel flow rate are determined, the burner outlet parameters are calculated, and the HPT inlet parameters are obtained by the cooling mixing model. 45 Obtain the pressure drop ratio; Similar to the HPC, the outlet parameters of the HPT were established. The calculation methods of the hybrid model and the LPT model were the same as the first hybrid model and the HPT model, respectively. After calculating the pressure loss at the LPT outlet through the pipeline model, the airflow finally expanded into the atmosphere; The eight convergence criteria selected include power balance, nozzle area, airflow consistency and gas path temperature. Under transient conditions, the rotor and temperature will change dynamically. Due to the dynamic compensation of rotational inertia and heat soak effect, it is possible to quantify and track sudden failures of rotating parts and bearings with high precision in the dynamic process so as to accurately monitor the health status.

8. An industrial gas turbine main rotating parts and bearing fault diagnosis system, characterized by: The system comprises: A setting module, wherein the setting module proposes a premise setting; a model building module, the model building module building an engine performance and degradation model; A fault diagnosis module, wherein the fault diagnosis module performs engine fault diagnosis; A verification module performs steady-state and transient model verification.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method of claims 1-7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method of claims 1-7 is implemented.

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