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WO2018044507A1 - Procédé à base de modèle assisté par apprentissage de machine pour estimer la durée de vie d'un composant de turbine à gaz - Google Patents

Procédé à base de modèle assisté par apprentissage de machine pour estimer la durée de vie d'un composant de turbine à gaz Download PDF

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Publication number
WO2018044507A1
WO2018044507A1 PCT/US2017/045685 US2017045685W WO2018044507A1 WO 2018044507 A1 WO2018044507 A1 WO 2018044507A1 US 2017045685 W US2017045685 W US 2017045685W WO 2018044507 A1 WO2018044507 A1 WO 2018044507A1
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WO
WIPO (PCT)
Prior art keywords
gas turbine
component
data
life
life consumption
Prior art date
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Ceased
Application number
PCT/US2017/045685
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English (en)
Inventor
Arindam Dasgupta
Anand A. Kulkarni
Amit Chakraborty
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Siemens Corp
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Siemens Corp
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Filing date
Publication date
Application filed by Siemens Corp filed Critical Siemens Corp
Publication of WO2018044507A1 publication Critical patent/WO2018044507A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]

Definitions

  • This disclosure relates generally to component life prediction, assessment, and evaluation of components.
  • a method is presented for estimating gas turbine component life.
  • the model-based approach involves empirical, analytical or numerical model creation in order to calculate the component's life.
  • external sources are needed to accompany the estimation such as the component's geometry, material data, and engine operating and health conditions.
  • Non-destructive testing (NDT) and destructive testing (DT) should be carried out using conventional techniques when the life fraction is greater than 0.5 (50%). Creep life estimation using the total life approach can be done by using either a life-based model, a strain-based model, or a damage-based model.
  • the service-based approach involves damage evaluation and remaining life assessment of the service exposed component, requiring direct access to the components, which means that the machine needs to be taken out of service.
  • the present status of the component's material is positioned within the standard scatter band either by measuring its properties, hence providing a refined prediction, or by direct assessment of the extent of the damage experienced by the component as a result of the actual service exposed.
  • the main methods of assessing the remaining component life will involve both non-destructive tests (NDTs) and destructive tests (DTs).
  • aspects of the present disclosure relate to a method and system for estimating the life consumption of a turbine component. Additionally, the disclosure relates to a method for assessing the current state of an operating gas turbine.
  • a method for estimating life consumption for a component of a gas turbine engine is provided. Data is acquired and stored relative to the current operating condition of a gas turbine component during operation of the gas turbine engine. The acquired data is then filtered and validated. At this point in the method, the life consumption of the component may be estimated.
  • a method for assessing the current state of an operating gas turbine is also provided.
  • the method as described above is performed for each of a plurality of gas turbine components that are cooperatively integrated on an operating gas turbine engine.
  • the current state of the engine is assessed using a statistical combination of the life consumption of each of the plurality of gas turbine components.
  • a system for estimating the life consumption of a plurality of gas turbine components in an operating gas turbine engine includes a processing unit, the processing unit including a program storage device and a processor.
  • the processor is in communication with a database that is used to store acquired data and is configured to be queried in real time to access the stored acquired data.
  • the program storage device embodies in a fixed tangible medium a set of program instructions executable by the processor to perform the steps of: acquiring and storing data relative to the current operating condition of the components during operating of the gas turbine engine, validating the acquired data, and determining life consumption of each component based on the acquired data.
  • the system also includes a control module having a processor communicatively coupled to the processing unit and configured to change a physical parameter of the gas turbine engine based on the life consumption of the components.
  • a display is configured to display the determined life consumption of each component.
  • FIG. 1 is a flowchart of the proposed method of estimating a life consumption for a gas turbine component
  • FIG. 2 is a flowchart of the proposed method of assessing the current state of a gas turbine engine
  • FIG. 3 is a diagram of an exemplary gas turbine indicating inclusion of sensors used to measure the performance of the turbine, and illustrating a life estimation and control system.
  • a first method is presented in which a life consumption of a gas turbine component in an operating gas turbine is estimated.
  • a flowchart of an embodiment of the first method may be seen in Fig. 1.
  • a second method in which the current state of an operating gas turbine engine is assessed.
  • a flowchart depicting the second method may be seen in Fig. 2.
  • a first step of the first method data is acquired and stored 100 relative to the current operating condition of the gas turbine component(s) of the gas turbine engine.
  • the data may be collected from a deployed gas turbine.
  • the 'component condition' represents the data and information available from conformal on-line continuous monitoring sensors and standard engine supervisory sensors for detailed operational history.
  • Event data include all historical data and records of the targeted gas turbine engines, such as history of faults, breakdowns, repairs, overhauls, start/stops etc.
  • COD may include all measured thermodynamics observations along the gas path and mechanical data, indicative of its current operating and health condition.
  • event data are not usually acquired in an automated manner and therefore, manual data entry may be required.
  • COD are normally collected by using available instrumentation on gas turbine engines, for example, vibration measurements through accelerometers, velocity pick-ups and displacement sensors; pressure measurements at various gas path stations of the gas turbine engines through pressure transducers; temperature measurements through thermocouples and/or resistance temperature detectors; shaft speed measurement; corrosion measurement through a corrosion probe; and ambient conditions measurements, for example, ambient pressure, temperature and relative humidity. It may be understood that other measurements may be taken by various sensors used to monitor the performance of a gas turbine.
  • Sensor measurements obtained from a gas turbine may contain random errors.
  • biases may be present in the measurements due to incorrect calibration and/or sensor faults.
  • the random noises and biases in the measured data give rise to inaccuracy in estimates of health parameters. To avoid this inaccuracy, the measured data should be validated before they are used.
  • the acquired measurements are filtered to reduce measurement noise and validated 200, through appropriate sensor validation techniques.
  • a good summary of state of the art noise filtering techniques and related signal processing processes is given in Vachtsevanos G., 'Intelligent Fault Diagnosis and Prognosis for Engineering Systems', John Wiley, New York, 2006.
  • the acquired data is filtered in order to remove random errors by averaging multiple data points collected over a certain time period.
  • the filtered data may then be validated.
  • Some validation techniques aim at detecting and isolating any possible sensor fault, while other validation techniques also allow an estimation of the underlying sensor bias.
  • Artificial Neural Networks such as described in Romessis C, Mathiousdakis K., 2003, 'Setting Up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation with Component Faults', ASME Journal of Engineering for Gas Turbines and Power, Vol 125, No. 3, July 2003, pp. 634-641, will be used to detect sensor anomalies and biases.
  • the data is processed to develop required boundary conditions for components and failure mechanisms, execute algorithms to establish component operational history lifing parameters (e.g., temperature and stress histories) which are then fed into damage mechanism lifing algorithms for estimation of consumed (and remaining) life.
  • component operational history lifing parameters e.g., temperature and stress histories
  • the algorithms and entire process are calibrated and verified with non-intrusive component sensor data, observed component condition, inspection and outage data, repair data, and fleet data.
  • the process flow involves data gathering and management, data processing, assessment and predictions followed by a feedback loop.
  • Engine components are affected by several different internal and external factors as they operate including local environmental conditions, fuel quality, and electrical demand patterns in the market served by the machine. During this process the engine and its components sustain different types and degree of damages induced by different well-known mechanisms. In that sense, these damages can be construed as 'life consumption' of the engine. In typical 'hot section' components these damage mechanisms may include corrosion, erosion, oxidation, hot corrosion, creep, low cycle fatigue, thermomechanical fatigue and high cycle fatigue. A complete and detailed assessment of the life consumed (or damage done) for each interval the machine operates and each start/stop cycle over long periods of operation is impossible.
  • Assessment 300, 400 can be simplified by adopting a machine-learning model, the 'box-model', where the damage done to engines by different operating modes may be subdivided into fatigue mechanisms and duration of operation mechanisms. Then, it is appropriate to subdivide engine operating modes into equivalent starts which accounts for all the fatigue damage, and equivalent baseload hours for the duration based life usage. With this subdivided counting system nearly all circumstances of gas turbine operation can be covered. The process results in two sets of equations for each component as follows:
  • LC onUne , LC 0 ff Une , LC start are life consumed during online, offline and start conditions determined by laboratory tests and physics-based modeling as a % of useful life
  • p onl i ne ⁇ poffime an( ⁇ p start are correc ti 0 n factors developed using machine learning techniques to operational data as applied to specific field conditions of individual machines.
  • the life consumption of each component may be computed based on a two term approach. Then, from a statistical combination of duty/load cycles of the engines and power/temperature conditions of all the components assessed, a life estimation of the gas turbine engine may be performed 400. A flowchart illustrating the second method of assessing the current state of the gas turbine may be seen in Fig. 2. From this assessment, a reliability of the gas turbine engine may be estimated.
  • the different damage mechanisms are assessed individually during operation and based on individual-use cases, maintenance decisions are formulated and implemented.
  • the damage mechanisms considered include corrosion, erosion, 2oxidation, hot corrosion, creep, HCF (high cycle fatigue), and off-line spinning.
  • the challenge and the success of the process depend on the estimation of the 'life consumption factors' and the 'correction factors' for the engine. Considering the large number of variables and range of these variables that are used as inputs into the physical models for estimation of these factors, computational complexity, and expense of the experimentation that will be required to comprehensively account for all conditions, a surrogate model approach for factor estimation is proposed.
  • a surrogate model may be used instead of analytical models as they may be too detailed and take too much time to update and execute in real-time when considering all the pertinent variables and their combinations that may affect the gas turbine engine.
  • analytical models may not be completely accurate when used for real conditions.
  • a surrogate model may be quickly updated without revisiting detailed analysis every time conditions in the system change such that the surrogate model may be quickly updated through re- evaluation of the correction factors should they change as the conditions change or, for example, as the gas turbine ages.
  • a surrogate model may be used to estimate the correction factors utilized in the equations (1) and (2) as described above, for example, the correction factors p onl i ne ⁇ poffime an( ⁇ p ⁇ a-rt usec j m - equation (2).
  • correction factors are those quantities that correct the theoretical formulations to real-life operation quantities and are unique to each machine.
  • the large dimensionality of the problem and input variation may be addressed using dimensional reduction techniques such as Auto Encoders (AE) and Artificial Neural Networks (ANN), or other Machine Learning methods that reduce engineering computation significantly.
  • AE Auto Encoders
  • ANN Artificial Neural Networks
  • ANNs can provide a nonlinear, physical model-free, multivariate surface data fitting learning algorithm that can automatically determine and learn the underlying functional relationship between inputs and outputs directly from data without a hypothesized functional form. Additionally, ANNs are suitable for adaptation to parallel computation architecture and have good generalization capabilities (universal approximators). They are also suitable for incremental learning, enabling the neural network models to be improved incrementally as new data become available or change previously calculated weight functions as input data changes (for example, if an engine starts to be operated differently as demand curve changes).
  • a surrogate model (which is really a set of correlations or a regression model) can be interrogated repeatedly and cheaply with engine operational data (time series data) to calculate cumulative life consumption - a real-time or almost real-time assessment method. The process will be also used predictively by using past patterns of engine use and projecting into the future.
  • the surrogate model may be utilized to predict the life consumption of each gas turbine component. From predicted life consumption of each gas turbine component, the life consumption of the gas turbine engine itself may be predicted 500.
  • the overall predictive tool development needs to be validated at two levels.
  • the first level is a validation of the results predicted by the deterministic tools that correlates to the sensor data (observed data) in the laboratory and the material degradation analysis. This is required because the surrogate model needs a training dataset that is larger than what a laboratory testing plan can economically provide. So, a large part of the training dataset will be provided from physics-based computational models which must be verified and adjusted themselves using a sampling method.
  • the second level of validation needs to accomplish a correlation between data generated in a controlled laboratory environment and observations from the field with their associated uncertainties. Surrogate models developed need to be updated accordingly and this is when ANNs are very useful as they can update themselves.
  • Results from the method may be output as a reporting value, for example, % of life consumed for each component.
  • a life estimation value for the gas turbine engine based on the life consumed for each component may be reported.
  • a change in the operating parameters of the gas turbine engine may be implemented based upon these life consumption numbers.
  • a variety of operating parameters that affect performance of the engine may include, for example, initiating a shutdown, reducing the load, and reducing the rotational speed of the engine. Such a change may be monitored via the sensors and controls on the turbine engine, for example, monitoring the exhaust temperature of the turbine engine.
  • FIG. 3 illustrates an embodiment of such a system.
  • the illustrated embodiment shows a simplified depiction of a typical gas turbine power plant 1 with a generator 2 supplying a plant electric load 3.
  • the generator 2 is driven by a shaft 4 powered by a gas turbine engine 5.
  • the gas turbine engine 5 is itself comprised of a large number of separate components, including a compressor 6, a combustion section 7, a turbine 8, and perhaps, a set of adjustable inlet vanes 9. Fuel is supplied to the combustion section 7 via a valve 10.
  • a plurality of sensors 11 are used to monitor the operation of the various components, passing the measured sensor readings to a separate control module 12.
  • the control module 12 may be co- located with the gas turbine power plant 1, or may be off-site from the turbine itself. As described above, the various sensors 11 measure many different parameters on the gas turbine engine 5, for example, measuring conditions such as temperature, pressure, rotation, vibration, etc.
  • the control module 12 receives inputs from the sensors 11 and transmits control signals to valves, motors, and actuators known in the art.
  • the control module 12 may include at least one processor.
  • the control module 12 may be in communication with a database 13 in order to store the sensor readings.
  • the acquired data may add up to several terabytes and thus requires a secure, organized database where the data may be easily stored and queried in real time.
  • the sensor database 13 may be linked to a life estimation and control system comprising an analysis processing platform 14, comprising at least one processor 18 and some form of memory 16.
  • the processing platform 14 outputs reporting values to a display 20 such as a visualization dashboard.
  • the processing platform 14 may send control signals to the control module 12 based on the life estimation analysis to change a physical parameter of the gas turbine engine 5 such as initiating a shutdown, reducing a load, etc.
  • the above described method may be implemented by program modules that are executed by a computer.
  • program modules include routines, objects, components, data structures and the like that perform particular tasks or implement particular abstract data types.
  • the term 'program' as used herein may connote a single program module or multiple program modules acting in concert.
  • the disclosed method of estimating the current state of life consumption for a component of a gas turbine engine as well as the corresponding system accomplishes a fast and accurate method of life estimation for the components as well as the system as a whole. Furthermore, the proposed method may enable a system to have a higher reliability at a lower or comparable cost. Additionally, the proposed method of estimating life consumption of components as well as the systems that the components make up may apply to a variety of commercial applications, not only the described embodiment of the gas turbine engine.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

La présente invention concerne un procédé et un système d'estimation d'une consommation de durée de vie pour un composant d'un moteur à turbine à gaz. Le procédé comprend l'acquisition et le stockage de données relatives à l'état de fonctionnement actuel du composant pendant le fonctionnement du moteur à turbine à gaz. Les données acquises sont ensuite validées. Sur la base des données acquises, la consommation de durée de vie du composant de turbine à gaz est estimée. L'invention concerne en outre un procédé d'évaluation d'un moteur à turbine à gaz qui est conduit au moyen de l'estimation de durée de vie des composants mesurés dans le procédé d'estimation de la consommation de durée de vie pour un composant.
PCT/US2017/045685 2016-08-29 2017-08-07 Procédé à base de modèle assisté par apprentissage de machine pour estimer la durée de vie d'un composant de turbine à gaz Ceased WO2018044507A1 (fr)

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CN109523171A (zh) * 2018-11-20 2019-03-26 未必然数据科技(北京)有限公司 一种基于svdd的燃气轮机进气系统健康度评估方法
CN110307969A (zh) * 2019-05-23 2019-10-08 哈尔滨理工大学 一种基于函数型数据拟合与卷积神经网络的行星齿轮故障预测方法
WO2019211288A1 (fr) * 2018-05-02 2019-11-07 Kongsberg Digital AS Procédé et système pour découvrir et visualiser des problèmes opérationnels potentiels de processus s'exécutant dans de l'équipement et des systèmes dans une installation
CN112287484A (zh) * 2020-10-29 2021-01-29 复旦大学 一种基于矢量代理模型的复杂工程系统可靠性设计方法
CN112595537A (zh) * 2020-12-17 2021-04-02 弥伦工业产品设计(上海)有限公司 基于信号分析的设备健康状态监控方法、系统及存储介质
IT202000004573A1 (it) * 2020-03-04 2021-09-04 Nuovo Pignone Tecnologie Srl Modello di rischio ibrido per l'ottimizzazione della manutenzione e sistema per l'esecuzione di tale metodo.
CN113361025A (zh) * 2021-04-28 2021-09-07 华东理工大学 一种基于机器学习的蠕变疲劳概率损伤评定方法
CN113469453A (zh) * 2021-07-19 2021-10-01 广州广日电梯工业有限公司 基于信息物理系统的电梯评估方法以及电梯评估装置
CN114818503A (zh) * 2022-05-06 2022-07-29 杉数科技(北京)有限公司 一种航空发动机的损伤发展预测方法及装置
CN115330094A (zh) * 2022-10-14 2022-11-11 成都秦川物联网科技股份有限公司 智慧燃气管道寿命预测方法、物联网系统、装置及介质
CN115659520A (zh) * 2022-11-11 2023-01-31 南京威翔科技有限公司 一种发动机寿命参数的处理方法
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CN119377732A (zh) * 2024-10-11 2025-01-28 新疆维吾尔自治区特种设备检验研究院 一种基于数据驱动的铬钼钒钢蠕变寿命预测方法

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Cited By (22)

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WO2019211288A1 (fr) * 2018-05-02 2019-11-07 Kongsberg Digital AS Procédé et système pour découvrir et visualiser des problèmes opérationnels potentiels de processus s'exécutant dans de l'équipement et des systèmes dans une installation
CN109523171B (zh) * 2018-11-20 2022-11-25 未必然数据科技(北京)有限公司 一种基于svdd的燃气轮机进气系统健康度评估方法
CN109523171A (zh) * 2018-11-20 2019-03-26 未必然数据科技(北京)有限公司 一种基于svdd的燃气轮机进气系统健康度评估方法
US11703421B2 (en) 2019-01-31 2023-07-18 Pratt & Whitney Canada Corp. System and method for validating component integrity in an engine
CN110307969A (zh) * 2019-05-23 2019-10-08 哈尔滨理工大学 一种基于函数型数据拟合与卷积神经网络的行星齿轮故障预测方法
US12222712B2 (en) 2020-03-04 2025-02-11 Nuovo Pignone Tecnologie—SRL Hybrid risk model for maintenance optimization and system for executing such method
IT202000004573A1 (it) * 2020-03-04 2021-09-04 Nuovo Pignone Tecnologie Srl Modello di rischio ibrido per l'ottimizzazione della manutenzione e sistema per l'esecuzione di tale metodo.
WO2021175493A1 (fr) * 2020-03-04 2021-09-10 Nuovo Pignone Tecnologie - S.R.L. Modèle de risque hybride pour une optimisation de maintenance et système pour exécuter un tel procédé
GB2609104A (en) * 2020-03-04 2023-01-25 Nuovo Pignone Tecnologie Srl Hybrid risk model for maintenance optimization and system for executing such method
CN112287484A (zh) * 2020-10-29 2021-01-29 复旦大学 一种基于矢量代理模型的复杂工程系统可靠性设计方法
CN112287484B (zh) * 2020-10-29 2021-12-07 复旦大学 一种基于矢量代理模型的复杂工程系统可靠性设计方法
CN112595537B (zh) * 2020-12-17 2023-03-21 弥伦工业产品设计(上海)有限公司 基于信号分析的设备健康状态监控方法、系统及存储介质
CN112595537A (zh) * 2020-12-17 2021-04-02 弥伦工业产品设计(上海)有限公司 基于信号分析的设备健康状态监控方法、系统及存储介质
CN113361025A (zh) * 2021-04-28 2021-09-07 华东理工大学 一种基于机器学习的蠕变疲劳概率损伤评定方法
CN113361025B (zh) * 2021-04-28 2024-03-29 华东理工大学 一种基于机器学习的蠕变疲劳概率损伤评定方法
CN113469453A (zh) * 2021-07-19 2021-10-01 广州广日电梯工业有限公司 基于信息物理系统的电梯评估方法以及电梯评估装置
CN113469453B (zh) * 2021-07-19 2024-05-10 广州广日电梯工业有限公司 基于信息物理系统的电梯评估方法以及电梯评估装置
CN114818503A (zh) * 2022-05-06 2022-07-29 杉数科技(北京)有限公司 一种航空发动机的损伤发展预测方法及装置
CN115330094A (zh) * 2022-10-14 2022-11-11 成都秦川物联网科技股份有限公司 智慧燃气管道寿命预测方法、物联网系统、装置及介质
US11898704B2 (en) 2022-10-14 2024-02-13 Chengdu Qinchuan Iot Technology Co., Ltd. Methods and Internet of Things systems for smart gas pipeline life prediction based on safety
CN115659520A (zh) * 2022-11-11 2023-01-31 南京威翔科技有限公司 一种发动机寿命参数的处理方法
CN119377732A (zh) * 2024-10-11 2025-01-28 新疆维吾尔自治区特种设备检验研究院 一种基于数据驱动的铬钼钒钢蠕变寿命预测方法

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