GB2627774A - Methods and system for optimizing scheduling of servicing events in turbine engines - Google Patents

Abstract

Ambient atmospheric conditions are monitored when the turbomachine is operating. A power factor is selected, which indicates a tolerable power factor reduction for the turbomachine. A real-time model generates at least one parameter indicative of a health condition of the turbomachine. A continuous-time model predicts a degradation effect on the parameter/s indicative of the respective health condition of the turbomachine. A value of a parameter indicative of a remaining useful life (RUL) of the turbomachine is estimated. This estimation is configured to optimize operational availability of the turbomachine while inhibiting occurrence of the predicted degradation effect. A schedule for servicing event, such as a component wash, replacement, or repair of a component, is defined on the basis of the RUL of the turbomachine

Description

METHODS AND SYSTEM FOR OPTIMIZING SCHEDULING

OF SERVICING EVENTS IN TURBINE ENGINES

BACKGROUND

[0001] Disclosed embodiments relate generally to the field of turbomachinery, such as may involve turbine engines, and, more particularly, to methods and system for optimizing scheduling of servicing events in the turbine engine.

[0002] Turbomachinery performance can generally degrade over time. For example, blades in a turbine engine (e.g., compressor blades, turbine blades, etc.) and other components can foul up due to, for example, deposition of dirt particles during engine operation. For example, the overall turbine engine performance can be affected because of, for example, reduction of compressor efficiency due to the fouling up of the compressor blades. The following are examples of patent literature that disclose certain known methods and systems in connection with servicing involving, for example, washing events in turbine engines: US 7,801,660 B2, "Methods and Systems for Estimating Compressor Fouling Impact to Combined Cycle Power Plants; US 2017/0191375 Al, "Gas Turbine Water Wash Methods and System; GB2502078B "Engine Wash Optimisation"; and WO 2013/127993 Al, "Method and System for Real-Time Gas Turbine Performance Advisory".

BRIEF SUMMARY

[0003] In one aspect, a computer-implemented method for optimizing scheduling of servicing events in a turbomachine is provided. The method allows: monitoring data indicative of ambient atmospheric conditions when the turbomachine is in operation; selecting a power factor indicative of a tolerable power factor reduction for the turbomachine, where the power factor is selected for the turbomachine subject to the ambient atmospheric conditions; running a real-time model of the turbomachine configured to generate at least one respective parameter indicative of a respective health condition of the turbomachine; predicting by way of a continuous-time model a respective degradation effect on the at least one respective parameter indicative of the respective health condition of the turbomachine; estimating a value of a parameter indicative of a remaining useful life of the turbomachine with respect to the power factor selected for the turbomachine subject to the ambient atmospheric conditions, where the estimating is configured to optimize operational availability of the turbomachine while inhibiting occurrence of the predicted degradation effect; and defining a schedule for a servicing event based on the estimated value of the respective parameter.

[0004] In another aspect, a system includes: a monitor configured to monitor data indicative of ambient atmospheric conditions when the turbomachine is in operation; a user interface configured to select a power factor indicative of a tolerable power factor reduction for the turbomachine, where the power factor is selected for the turbomachine subject to the ambient atmospheric conditions; a real-time model of the turbomachine configured to generate at least one respective parameter indicative of a respective health condition of the turbomachine; a continuous-time model configured to predict a respective degradation effect on the at least one respective parameter indicative of the respective health condition of the turbomachine; an estimator is configured to estimate a value of a parameter indicative of a remaining useful life of the turbomachine with respect to the power factor selected for the turbomachine subject to the ambient atmospheric conditions, where the estimator is configured to optimize operational availability of the turbomachine while inhibiting occurrence of the predicted degradation effect; and a scheduler configured to define a schedule for a servicing event based on the estimated value of the parameter.

100051 In yet another aspect, a computer-implemented method for optimizing scheduling of servicing events for a turbomachine including at least one of a compressor and a turbine is provided. A non-transitory computer readable medium is programmed with computer-readable code so that when a computer processor executes the computer-readable code, the computer processor performs the steps of: monitoring data indicative of ambient atmospheric conditions when the turbomachine is in operation; selecting a power factor indicative of a tolerable power factor reduction for the turbomachine, where the power factor is selected for the turbomachine subject to the ambient atmospheric conditions; running a real-time model of the turbomachine configured to generate at least one respective parameter indicative of a respective health condition of the turbomachine; running a continuous-time model configured to predict a respective degradation effect on the at least one respective parameter indicative of the respective health condition of the turbomachine, where the at least one respective parameter indicative of the respective health condition of the turbomachine comprises at least one of the following: compressor efficiency, compressor flow capacity, turbine efficiency, turbine flow capacity; estimating a value of a parameter indicative of a remaining useful life of the turbomachine with respect to the power factor selected for the turbomachine subject to the ambient atmospheric conditions, where the estimating is configured to optimize operational availability of the turbomachine while inhibiting occurrence of the predicted degradation effect; and defining a schedule for a servicing event based on the estimated value of the parameter.

100061 The foregoing has broadly outlined some of the technical features of the present disclosure so that those skilled in the art may better understand the detailed description that follows. Additional features and advantages of the disclosure will be described hereinafter that form the subject of the claims. Those skilled in the art will appreciate that they may readily use the conception and the specific embodiments disclosed as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Those skilled in the art will also realize that such equivalent constructions do not depart from the spirit and scope of the disclosure in its broadest form.

100071 Also, before undertaking the Detailed Description below, it should be understood that various definitions for certain words and phrases are provided throughout this patent document, and those of ordinary skill in the art will understand that such definitions apply in many, if not most, instances to prior as well as future uses of such defined words and phrases. While some terms may include a wide variety of embodiments, the appended claims may expressly limit these terms to specific embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] FIG. 1 is a schematic of one example of turbomachinery, such as a turbine engine, that can benefit from disclosed embodiments of methods and system for optimizing scheduling of servicing events in the turbine engine.

[0009] FIG. 2 is a flow chart showing example steps in connection with one example embodiment of a disclosed method.

100101 FIG. 3 is a block diagram representation of example structural and/or operational relationships that may be involved in disclosed embodiments.

[0011] FIG. 4 shows respective plots as a function of time illustrating an example relationship between reduction of available turbine engine power and compressor efficiency degradation (AEff).

[0012] FIG. 5 shows respective plots indicative of respective relationships between available engine power and compressor efficiency for various example cases involving different ambient temperature conditions.

100131 FIG. 6 shows relative turbine engine power as a function of ambient temperature for various example cases of respective percentages of load ratings.

DETAILED DESCRIPTION

[0014] Disclosed embodiments offer a reliable and cost-effective technical solution for appropriately optimizing scheduling of servicing events, such as a component wash event, a component repair, a component replacement, etc., in connection with turbine engines, e.g., gas turbine engines. To recover engine performance, it is known that users can periodically carry out servicing events, such as on-line or off-line washing of certain components of the turbine engine, e.g., compressor blades, turbine blades, etc. These periodic servicing events, however, can unduly curtail productive utilization of the turbine engine.

100151 Disclosed embodiments are designed to offer innovative methodology conducive to appropriate reduction of turbine engine shutdowns and increased engine availability. The basic idea is to appropriately balance trade-offs (often involving countervailing tradeoffs), such as may involve increasing engine life and operational availability of the engine while maintaining appropriate engine performance, e.g., engine efficiency, etc., under varying conditions, such as may involve varying atmospheric ambient conditions.

[0016] Disclosed embodiments involve real-time turbine engine optimization, which can offer users a substantially enhanced decision making capability with respect to the utilization of their assets (e.g., turbomachinery equipment) while appropriately considering conflicting objectives, such as engine performance recovery versus engine availability.

100171 Disclosed embodiments can offer estimation of Remaining Useful Life (RUL), that, for example, appropriately considers blade fouling degradation or other forms of degradation, based on linear or non-linear regression modelling. The RUL estimation may be statistically bounded by respective confidence intervals calculated in connection with respective health parameters of a given subsystem of the turbine engine, such as compressor flow capacity, compressor efficiency, turbine efficiency, turbine flow capacity, in case the subsystem involved includes a compressor, a turbine, etc. In another aspect, disclosed embodiments can be arranged to continuously perform online RUL estimation. For example, predictions involving parameters indicative of health of components of the turbine engine, such as compressor efficiency and compressor flow capacity, can be continually updated using virtual data from a model of the engine configured to operate in real-time.

100181 The present ifiVelliOf has recognized that utilization of virtual data generated by the model in real-time is conducive to optimization in real-time of the turbine engine, so that, for example, engine usage life can be appropriately extended (e.g., maximized), and in turn component deterioration can be appropriately reduced (e.g., minimized) notwithstanding that the turbine engine may be subject to varying conditions.

[0019] The present inventor has further recognized that, as inued above, servicing events can disrupt a full productive utilization of the turbine engine, and hence disclosed embodiments offer methodology for appropriate optimization of turbine engine performance with respect to operational availability of the turbine engine. Therefore, disclosed embodiments contribute to reduction of planned outages for performing servicing events and recommends such washing events when doing so appropriately meets desired optimization objectives so that, for example, operational availability of the turbine engine can be appropriately maximized, and health degradation of the turbine engine can be appropriately minimized notwithstanding that the turbine engine may be subject to varying conditions.

[0020] At least in view of the foregoing considerations, the present inventor discloses embodiments of system and methods for optimizing scheduling of servicing events in a turbomachine that, for example, consider actual ambient and actual operating conditions (which can vary significantly from application to application, e.g., power generation, mechanical drive) and from customer to customer even for the same application. Consequently, disclosed embodiments can more accurately and consistently estimate consumed life of, for example, turbine engine components subject to fouling conditions and other modalities of degradation. Moreover, disclosed embodiments can carry out life estimation and subsequent prognostics of remaining useful life of turbine engine components in real-time, and this in turn is conducive to the implementation of real-time optimization methods that can offer to users a more confident decision-making capability with respect to cost-effective utilization of their turbomachinery assets.

[0021] Before disclosed embodiments are explained in detail, it is to be understood that disclosed embodiments are not limited in their application to the details of construction and the arrangement of components set forth in this description or illustrated in the following drawings. Disclosed embodiments are capable of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0022] Various technologies that pertain to disclosed embodiments will now be described with reference to the drawings, where like reference numerals represent like elements throughout. The drawings discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged apparatus. It is to be understood that functionality that is described as being carried out by certain system elements may be performed by multiple elements. Similarly, for instance, an element may be configured to perform functionality that is described as being carried out by multiple elements. The numerous innovative teachings of the present application will be described with reference to exemplary non-limiting embodiments.

100231 It should be understood that the words or phrases used herein should be construed broadly, unless expressly limited in some examples. For example, the terms "including," "having," and "comprising," as well as derivatives thereof, mean inclusion without limitation. The singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Further, the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. The term "or" is inclusive, meaning and/or, unless the context clearly indicates otherwise. The phrases "associated with" and "associated therewith," as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, or the like. Furthermore, while multiple embodiments or constructions may be described herein, any features, methods, steps, components, etc. described with regard to one embodiment are equally applicable to other embodiments absent a specific statement to the contrary.

[0024] Also, although the terms "first-, "second", "third" and so forth may be used herein to refer to various elements, information, functions, or acts, these elements, information, functions, or acts should not be limited by these terms. Rather these numeral adjectives are used to distinguish different elements, information, functions or acts from each other. For example, a first element, information, function, or act could be termed a second element, information, function, or act, and, similarly, a second element, information, function, or act could be termed a first element, information, function, or act, without departing from the scope of the present disclosure.

[0025] In addition, the term "adjacent to" may mean that an element is relatively near to but not in contact with a further element or that the element is in contact with the further portion, unless the context clearly indicates otherwise. Further, the phrase "based on" is intended to mean "based, at least in part, on" unless explicitly stated otherwise. Terms "about" or "substantially" or like terms are intended to cover variations in a value that are within normal industry manufacturing tolerances for that dimension. If no industry standard is available, a variation of twenty percent would fall within the meaning of these terms unless otherwise stated.

100261 It is noted that while the instant disclosure includes a description in the context of a fully functional system and/or a series of acts, those skilled in the art will appreciate that at least portions of the mechanism of the present disclosure and/or described acts may be capable of being distributed in the form of computer/processor executable instructions (e.g., software/firmware applications) contained within a storage device that corresponds to a non-transitory machine-usable, computer-usable, or computer-readable medium in any of a variety of forms (e.g., flash memory, SSD, hard drive). The computer/processor executable instructions may include a routine, a sub-routine, programs, applications, modules, libraries, and/or the like. Further, it should be appreciated that computer/processor executable instructions may correspond to and/or may be generated from source code, byte code, runtime code, machine code, assembly, Java, JavaScript, Python, Rust, Swift, Go, C, C#, C++ or any other form of code that can be programmed/configured to cause at least one processor to carry out the acts and features described herein. Still further, results of the described/claimed processes or functions may be stored in a computer-readable medium, displayed on a display device, and/or the like.

[0027] It should be appreciated that acts associated with the above-described methodologies, features, and functions (other than any described manual acts) may be carried out by one or more data processing systems via operation of one or more of the processors. Thus, it is to be understood that when referring to a data processing system or control system such a system may be implemented across several data processing systems organized in a distributed system in communication with each other directly or via a network.

100281 As used herein a processor or processor module corresponds to any electronic device that is configured via hardware circuits, software, and/or firmware to process data. For example, processors described herein may correspond to one or more (or a combination) of a microprocessor, CPU, or any other integrated circuit (IC) or other type of circuit that is capable of processing data in a data processing system. As discuss previously, the processor that is described or claimed as being configured to carry out a particular described/claimed process or function may correspond to a CPU that executes computer/processor executable instructions stored in a memory in the form of software to carry out such a described/claimed process or function. However, it should also be appreciated that such a processor may correspond to an IC that is hardwired with processing circuitry (e.g., an FPGA or ASIC IC) to carry out such a described/claimed process or function. Also, it should be understood, that reference to a processor may include multiple physical processors or cores that are configured to carry out the functions described herein. In addition, it should be appreciated that a data processing system and/or a processor may correspond to a controller that is operably configured to control at least one operation including a programable logic controller (PLC).

[0029] In addition, it should also be understood that a processor or processor module that is described or claimed as being configured to carry out a particular described/claimed process or function may correspond to the combination of the processor with the executable instructions (e.g., software/firmware applications) loaded/installed into the described memory (volatile and/or non-volatile), which are currently being executed and/or are available to be executed by the processor to cause the processor to carry out the described/claimed process or function. Thus, a processor that is powered off or is executing other software, but has the described software loaded/stored in a storage device in operative connection therewith (such as in a flash memory, SSD, or hard drive) in a manner that is available to be executed by the processor (when started by a user, hardware and/or other software), may also correspond to the described/claimed processor that is operably configured to carry out the particular processes and functions described/claimed herein.

[0030] Those of ordinary skill in the art will appreciate that hardware and software depicted in connection with disclosed embodiments may vary for particular implementations. The depicted examples are provided for the purpose of explanation only and are not meant to imply architectural limitations with respect to the present disclosure. Also, those skilled in the art will recognize that, for simplicity and clarity, the full structure and operation of all data processing systems suitable for use with the present disclosure is not being depicted or described herein. Instead, only so much of a data processing system as is unique to the present disclosure or necessary for an understanding of the present disclosure is depicted and described. The remainder of the construction and operation of the data processing system may conform to any of the various current implementations and practices known in the art.

[0031] FIG. 1 shows one non-limiting example of turbomachinery, such as a turbine engine 100, that can benefit from disclosed embodiments for optimizing scheduling of servicing events in the turbine engine. Examples of servicing events without limitation may include a component wash event, a component repair, a component replacement, etc., For example, over time certain engine components become fouled by deposits, which reduce engine efficiency and thereby increase the amount of fuel consumed for a given application. Thus, the engine becomes more expensive to operate. As would be appreciated by one skilled in the art, an engine wash event is defined as an event during which certain components of the turbine engine are flushed through with water or any other suitable cleaning fluid to remove at least some of the fouling material from the engine components. It will be appreciated that disclosed embodiments are not limited to any specific type of turbomachinery or limited to wash events in connection with any specific component of the turbine engine.

100321 Turbine engine 100 comprises, in flow series, an inlet 12, a compressor 101, a combustor 102 and a turbine 103 which are generally arranged in flow series and generally in the direction of a longitudinal or rotational axis 20. The turbine engine 100 further comprises a shaft 22 which is rotatable about the rotational axis 20 and which extends longitudinally through the turbine engine 100. The shaft 22 drivingly connects the turbine 103 to the compressor 101.

[0033] The terms upstream and downstream refer to the flow direction of the airflow and/or working gas flow through the engine unless otherwise stated. The terms forward and rearward refer to the general flow of gas through the engine. The terms axial, radial and circumferential are made with reference to a rotational axis 20 of the engine.

1003411n operation of turbine engine 100, a flow of air 24, which is taken in through the air inlet 12 is compressed by the compressor 101 and delivered to the combustor 102 comprising a burner section 16. The burner section 16 comprises a burner plenum 26, one or more combustion chambers 28 that may be defined by a double wall can 27 and at least one burner 30 fixed to each combustion chamber 28. The combustion chambers 28 and the burners 30 are located inside the burner plenum 26. The compressed air passing through the compressor 12 enters a diffuser 32 and is discharged from the diffuser 32 into the burner plenum 26 from where a portion of the air enters the burner 30 and is mixed with a gaseous or liquid fuel. The air/fuel mixture is then burned and the combustion gas 34 or working gas from the combustion is channelled via a transition duct 35 to the turbine 103.

100351 The turbine 103 comprises a number of blade-carrying discs 36 attached to the shaft 22. In the present example, two discs 36 each carry an annular array of turbine blades 38. However, the number of blade-carrying discs could be different, e.g., just one disc or more than two discs. In addition, guiding vanes 40, which are fixed to a stator 42 of the turbine engine 100, are disposed between the turbine blades 38. Between the exit of the combustion chamber 28 and the leading turbine blades 38 inlet guiding vanes 44 are provided.

[0036] The combustion gas from the combustion chamber 28 enters the turbine 103 and drives the turbine blades 38 which in turn rotates the shaft 22. The guiding vanes 40, 44 serve to optimise the angle of the combustion or working gas on to the turbine blades 38. Compressor 101 comprises an axial series of guide vane stages 46 and rotor blade stages 48. As noted above, the overall turbine engine performance can be affected because of reduction of compressor efficiency and/or compressor flow capacity due to the fouling of the compressor blades, for example.

100371 The non-limiting example of the turbomachinery shown in FIG. 1 further includes a controller or control system 110 operatively coupled to turbine engine 100. Control system 110 comprise one or more processors or processing units 102, memory 104, and computer-readable media, such as non-transitory machine-usable media. Control system 110 constitutes a computing system that executes programs and operations to control operation of turbine engine 100 using sensor inputs, scheduling algorithms, control models and/or commands from human operators. The programs and functions executed by the control system 110 may include, among others, sensing and/or modeling operating parameters, operational boundaries, applying operational boundary models, applying scheduling algorithms and applying boundary control logic.

100381 FIG. 2 is a flow chart 200 depicting example steps (e.g., involving structural and/or operational relationships) in connection with one example embodiment of a disclosed computer-implemented method for optimizing scheduling of servicing events in a turbomachine. Subsequent to start step 202, step 204 allows monitoring data indicative of ambient atmospheric conditions when the turbomachine is in operation. The monitored data by way of example may include ambient temperature, pressure, altitude and relative humidity. Step 206 allows selecting, e.g., by way of a user interface, a power factor indicative of a tolerable power factor reduction (e.g., a maximum tolerable power factor reduction under the applicable ambient atmospheric conditions) for the turbomachine. The power factor is selected for the turbomachine subject to the ambient atmospheric conditions. For the selected power factor, step 208 allows running a real-time model of the turbomachine configured to generate at least one respective parameter indicative of a respective health condition of the turbomachine.

100391 As would be appreciated by one skilled in the art, a real-time model can provide a simplified representation of a real-world entity (for example, the turbine engine) by way of functional relationships utilizing, for example, computer code and can serve to assess the time-varying behavior of the turbine engine. As would be further appreciated by one skilled in the art, real-time or real-time describes various operations in computing or other processes that ensure a response within a certain specified time to, for example, appropriately analyze and react to the time-varying behavior of the turbine engine.

100401 Returning to flow chart 200 in FIG. 2, step 209 allows predicting, such as by way of a continuous-time model, a respective degradation effect on the at least one respective parameter indicative of the respective health condition of the turbomachine. It will be appreciated that a continuous time model treats time as a continuum, and this inhibits jumps or discontinuities in the time domain. As will be appreciated by those skilled in the art, a continuous-time modelling formulation is conducive to provide a relatively more accurate solution compared to a formulation including jumps or discontinuities in the time domain. That is, given a sufficient number of time points, continuous-time models will ensure finding the optimal solution.

[0041] Step 210 allows estimating a value of a parameter indicative of a remaining useful life (RUL) of the turbomachine with respect to the power factor selected for the turbomachine subject to the ambient atmospheric conditions. This estimating is configured to optimize operational availability of the turbomachine while inhibiting occurrence (e.g., a future occurrence) of the predicted degradation effect. Prior to return step 214, step 212 allows defining a schedule for a servicing event based on the estimated value of the RUL parameter. The defined schedule may be viewed as a suggestion since the operator has discretion as to whether actually carrying out the defined servicing event is desirable under any specific circumstances that may be involved.

[0042] In one non-limiting embodiment, the continuous-time model is based on a suitable statistical regression analysis. By way of example, the regression model predicts future evolution of certain parameters (such as compressor efficiency and compressor flow capacity, etc.), using present (and indirectly historical) data for predicting the future evolution of, for example, compressor efficiency and compressor flow capacity.

100431 In one example embodiment, the continuous-time model is configured to implement a linear degradation model that without limitation may be characterized as follows: [0044] S(t)=.+0(t)t+E(t) [00451 where: [0046] -SO) represents the estimated RUL parameter [0047] -4 represents a model intercept, which is a constant.

[0048] -9(t) represents a model slope that is modeled as a random variable with a normal distribution with certain mean and variance.

[0049] -a(t) represents model additive noise that is modeled as a normal distribution with zero mean and certain variance.

100501 In one example embodiment, the continuous-time model is based on a non-linear regression model, such as may involve exponential regression. It will be appreciated that such models may be used in complementary fashion to, for example, appropriately characterize different types of degradation modalities.

[0001] In one example embodiment, the continuous-time exponential degradation model without limitation may be characterized as follows: 100021 S(t)=++0(0eA(13(t)t+E(t)-c7/2 100031 where: 100041 -S(t) represents the estimated RUL parameter [0005] -(I) represents a model intercept, which is a constant. Parameter 4) can be initialized as a lower bound or as an upper bound on a feasible region of the degradation variable. 100061-0(0 represents a random variable that is modeled as a lognormal distribution with certain mean and variance.

[0007] If the sign of e is positive, then 4) is a lower bound. If the sign of 0 is negative, then (I) is an upper bound.

[0008] -0(t) represents a random variable modeled as a Gaussian distribution with certain mean and variance.

[0009] -a(t) represents model additive noise that is modeled as a normal distribution with zero mean and certain variance.

[0010] -cs represents noise variance.

[0011] FIG. 3 is a block diagram representation of example structural and/or operational relationships involved in disclosed embodiments. Block 300 in FIG. 3 represents an optimizer subsystem configured to implement the method described above in the context of FIG. 2. Block 300 may be part of a larger system of the turbine engine that, for example, may be configured to evaluate, analyze, and recommend actions in connection with the overall health of the engine. In one example embodiment optimizer 300 includes a processor module 310 operatively coupled to a monitor 312 configured to monitor data indicative of ambient atmospheric conditions when the turbomachine is in operation. Processor module 310 may be further operatively coupled to a user interface 314 configured to select a power factor indicative of a tolerable power factor reduction (e.g., a maximum tolerable power factor reduction under the applicable ambient atmospheric conditions) for the turbomachine. In one example embodiment, the power factor selected for the turbomachine is subject to the ambient atmospheric conditions.

100121In one example embodiment, processor module 310 includes or is otherwise operatively coupled to a real-time model 316 of the turbomachine to generate at least one respective parameter indicative of a respective health condition of the turbomachine, such as compressor efficiency, compressor flow capacity, turbine efficiency, turbine flow capacity, etc. Processor module 310 further includes or is otherwise operatively coupled to a continuous-time model 317 configured to predict a respective degradation effect on the at least one respective parameter indicative of the respective health condition of the turbomachine. For example, AEff and ACap respectively represent predicted degradation effects on compressor efficiency and compressor flow capacity. An estimator 318 is configured to process the parameters (e.g., AEff and ACap) indicative of the respective health conditions of the turbomachine to estimate a value of a parameter indicative of a remaining useful life (RUL) of the turbomachine with respect to the power factor selected for the turbomachine subject to the ambient atmospheric conditions. Estimator 318 is configured to optimize operational availability of the turbomachine while inhibiting occurrence of the predicted degradation effect. A scheduler 320 is configured to define a schedule for a servicing event based on the estimated value of the RUL parameter. As noted above, the schedule defined by scheduler 320 may be viewed as a suggestion since the operator has the discretion whether or not to actually carry out the defined wash event.

[0013] Available power reduction, such as due to compressor fouling, is a progressive process and increases with the running time of the turbine engine. In one example embodiment, as shown in FIG. 4, this reduction of available power may be related to AEff (compressor efficiency degradation). As noted above, the reduction of available power may also be related to ACap (compressor flow capacity degradation). In a general case, the power reduction in a given turbine engine may be driven by different degradation mechanisms, (e.g., fouling, erosion, etc.) and therefore, estimation of the RUL parameter for a given minimum acceptable power would depend on the specific degradation rates applicable to the given turbine engine while operating in a specific environment.

[0014] FIG. 5 shows respective plots indicative of respective relationships between available engine power and compressor efficiency for various example cases involving different ambient temperature conditions.

100151 It can be appreciated that the optimization space can be represented by a 2D matrix to account for ambient atmospheric conditions, e.g., ambient temperature, etc. This allows accounting for the fact that the turbine engine will typically experience power reduction with increased ambient temperature, or conversely, will typically experience power increase with reduced ambient temperature, as shown in FIG. 6, which shows relative turbine engine power as a function of ambient temperature for various example cases of respective percentages of load ratings.

[0016] In operation, disclosed embodiments are effective for performing real-time turbine engine optimization, which offers users a more reliable decision-making capability with respect to productive utilization of their assets considering conflicting objectives such as appropriate performance recovery versus asset availability (e.g., substantially reduced downtime).

100171 In operation, disclosed embodiments offer by way of example: superior prognostics capability based on degradation modelling (for example, a linear model could be enhanced with a non-linear regression modelling approach tailored for certain degradation modes).

[0018] In operation, disclosed embodiments enable users to configure and run their turbine engines in an appropriately optimized manner while staying clear from unduly conservative and (thus relatively) costlier operation/maintenance techniques for performing servicing events.

[0019] In operation, disclosed embodiments enable RUL estimation bounded with confidence interval applicable to health parameters of interest, such as compressor flow capacity and compressor efficiency. Additionally, disclosed embodiments enable continuous online prognosis of RUL based on real-time optimization that, without limitation, uses virtual data generated in real-time by a model of the engine and considers a maximum tolerable power reduction in view of actual operating ambient conditions and the degradation rates in connection with such health parameters.

[0020] It will be appreciated that --although the foregoing disclosure for purposes of conceptual explanation has primarily focused in one optimization example, such as involving wash events, to restore performance that has degraded due to the fouling up of certain components in the compressor--disclosed embodiments can be adapted for various optimization applications that may be related to different components (e.g., the turbine), and may involve different degradation modes, e.g., erosion, etc. 100211 As will be appreciated by one skilled in the art, unlike degradation due to the fouling up of components, erosion is one example of an irreversible degradation mode where the affected component must be repaired/replaced during service; however, regardless of whether the degradation modality is reversible or irreversible, disclosed embodiments can be readily adapted to provide an optimized service schedule balancing in an optimal manner power recovery vs service downtime. In this example application involving erosion, the relevant health parameters are turbine efficiency and turbine flow capacity. For example, this degradation mode manifests itself by way of reduced turbine efficiency and increased turbine flow capacity; the key point being that disclosed embodiments can be broadly applied to any situation involving health parameters (e.g., efficiency and capacity) generated by the real-time model of the turbine engine, with subsequent degradation prognostics based on (linear and/or non-linear) regression modeling to support appropriate optimization logic, such as appropriate power recovery vs availability (reduced service downtime).

[0022] Although at least one exemplary embodiment of the present disclosure has been described in detail, those skilled in the art will understand that various changes, substitutions, variations, and improvements disclosed herein may be made without departing from the scope of the disclosure in its broadest form.

[0023] None of the description in the present application should be read as implying that any particular element, step, act, or function is an essential element, which must be included in the claim scope. The scope of patented subject matter is defined only by the allowed claims. Moreover, none of these claims are intended to invoke a means plus function claim construction unless the exact words "means for" are followed by a participle.

Claims (24)

1. CLAIMSWhat is claimed is: -I. A computer-implemented method for optimizing scheduling of servicing events in a turbomachine, the method comprising: monitoring data indicative of ambient atmospheric conditions when the turbomachine is in operation; selecting a power factor indicative of a tolerable power factor reduction for the turbomachine, the power factor selected for the turbomachine subject to the ambient atmospheric conditions; for the power factor selected for the turbomachine subject to the ambient atmospheric conditions, running a real-time model of the turbomachine configured to generate at least one respective parameter indicative of a respective health condition of the turbomachine; predicting by way of a continuous-time model a respective degradation effect on the at least one respective parameter indicative of the respective health condition of the turbomachine; estimating a value of a parameter indicative of a remaining useful life of the turbomachine with respect to the power factor selected for the turbomachine subject to the ambient atmospheric conditions, wherein the estimating is configured to optimize operational availability of the turbomachine while inhibiting occurrence of the predicted degradation effect; and defining a schedule for a servicing event based on the estimated value of the parameter.
2. 2. The method according to claim 1, wherein the turbomachine comprises at least one of a compressor and a turbine.
3. 3. The method according to claim 2, wherein the at least one respective parameter indicative of the respective health condition of the turbomachine comprises at least one of the following: compressor efficiency, compressor flow capacity, turbine efficiency, turbine capacity.
4. 4. The method according to claim 1, wherein the continuous-time model is based on a linear regression model to predict the respective degradation effect.
5. 5. The method according to claim I, wherein the continuous-time model is based on a non-linear regression model to predict the respective degradation effect.
6. 6. The method according to claim 5, wherein the non-linear regression model comprises exponential regression.
7. 7. The method according to claim 1, wherein the estimating comprises calculating a confidence interval for the estimated value of the parameter indicative of the remaining useful life of the turbomachine.
8. 8. The method according to claim 1, wherein the servicing event is a wash event.
9. 9. The method according to claim 8, wherein the wash event is selected from the group consisting of an online wash event and an offline wash event.
10. 10. The method according to claim 1, wherein the data indicative of the ambient atmospheric conditions are selected from the group consisting of ambient temperature, pressure, altitude and relative humidity.
11. 11. The method according to claim 1, where the servicing event is selected from the group consisting of a component wash event, a component repair event, and a component replacement event.
12. 12. A system comprising: a monitor configured to monitor data indicative of ambient atmospheric conditions when the turbomachine is in operation; a user interface configured to select a power factor indicative of a tolerable power factor reduction for the turbomachine, the power factor selected for the turbomachine subject to the ambient atmospheric conditions; a real-time model of the turbomachine configured to generate at least one respective parameter indicative of a respective health condition of the turbomachine; a continuous-time model configured to predict a respective degradation effect on the at least one respective parameter indicative of the respective health condition of the turbomachine; an estimator configured to estimate a value of a parameter indicative of a remaining useful life of the turbomachine with respect to the power factor selected for the turbomachine subject to the ambient atmospheric conditions, wherein the estimator is configured to optimize operational availability of the turbomachine while inhibiting occurrence of the predicted degradation effect; and a scheduler configured to define a schedule for a servicing event based on the estimated value of the parameter.
13. 13. The system according to claim 12, wherein the turbomachine comprises at least one of a compressor and a turbine.
14. 14. The system according to claim 13, wherein the at least one parameter indicative of the respective health condition of the turbomachine comprises at least one of the following: compressor efficiency, compressor flow capacity, turbine efficiency, turbine capacity.
15. 15. The system according to claim 12, wherein the continuous-time model is based on a linear regression model to predict the respective degradation effect.
16. 16. The system according to claim 12, wherein the continuous-time model is based on a non-linear regression model to predict the respective degradation effect.
17. 17. The system according to claim 16, wherein the non-linear regression model comprises exponential regression.
18. 18. The system according to claim 12, wherein the estimator further comprises a calculator configured to calculate a confidence interval for the estimated value of the parameter indicative of the remaining useful life of the turbomachine.
19. 19. The system according to claim 12, wherein the data indicative of the ambient atmospheric conditions are selected from the group consisting of ambient temperature, pressure, altitude and relative humidity.
20. 20. The method according to claim 12, where the servicing event is selected from the group consisting of a component wash event, a component repair event, and a component replacement event.
21. 21. A computer-implemented method for optimizing scheduling of servicing events in a turbomachine including at least one of a compressor and a turbine, where a non-transitory computer readable medium is programmed with computer-readable code so that when a computer processor executes the computer-readable code, the computer processor performs the steps of: monitoring data indicative of ambient atmospheric conditions when the turbomachine is in operation; selecting a power factor indicative of a tolerable power factor reduction for the turbomachine, the power factor selected for the turbomachine subject to the ambient atmospheric conditions; running a real-time model of turbomachine configured to generate at least one respective parameter indicative of a respective health condition of the turbomachine; running a continuous-time model configured to predict a respective degradation effect on the at least one respective parameter indicative of the respective health condition of the turbomachine, wherein the at least one respective parameter indicative of the respective health condition of the turbomachine comprises at least one of the following: compressor efficiency, compressor flow capacity, turbine efficiency, turbine capacity; estimating a value of a parameter indicative of a remaining useful life of the turbomachine with respect to the power factor selected for the turbomachine subject to the ambient atmospheric conditions, wherein the estimating is configured to optimize operational availability of the turbomachine while inhibiting occurrence of the predicted degradation effect; and defining a schedule for a servicing event based on the estimated value of the parameter.
22. 22. The computer-implemented method according to claim 21, wherein the continuous-time model is based on a linear regression model to predict the respective degradation effect.
23. 23. The computer-implemented method according to claim 2 1, wherein the continuous-time model is based on a non-linear regression model to predict the respective degradation effect.
24. 24. The computer-implemented method according to claim 21, wherein the estimating comprises calculating a confidence interval for the estimated value of the parameter indicative of the remaining useful life of the turbomachine.