System and method for monitoring health of airfoils

A method for monitoring the health of one or more blades is presented. The method includes the steps of generating a signal representative of delta times of arrival corresponding to the rotating blade, generating a reconstructed signal by decomposing the signal representative of the delta times of arrival utilizing a multi-resolution analysis technique, wherein the reconstructed signal is representative of at least one of static deflection and dynamic deflection in the rotating blade.

BACKGROUND

Embodiments of the disclosure relate generally to systems and methods for monitoring health of rotor blades or airfoils.

Rotor blades or airfoils play a crucial role in many devices with several examples, such as, axial compressors, turbines, engines and turbo-machines. For example, an axial compressor typically has a series of stages with each stage comprising a row of rotor blades followed by a row of static blades. Accordingly, each stage generally comprises a pair of rotor blades and static blades. As an illustrative axial compressor example, the rotor blades increase the kinetic energy of a fluid that enters the axial compressor through an inlet. Furthermore, the static blades generally convert the increased kinetic energy of the fluid into static pressure through diffusion. Accordingly, the rotor blades and static blades play an important role to increase the pressure of the fluid.

The rotor blades and the static blades (hereinafter “blades”) are used in wide and varied applications of the axial compressors that include the blades. Axial compressors, for example, may be used in a number of applications, such as, land based gas turbines, jet engines, high speed ship engines, small scale power stations, and the like. In addition, the axial compressors may be used in varied applications, such as, large volume air separation plants, blast furnace air, fluid catalytic cracking air, propane dehydrogenation, and the like.

The blades operate for long hours under extreme and varied operating conditions such as, high speed, pressure and temperature that effect the health of the blades. In addition to the extreme and varied conditions, certain other factors lead to fatigue and stress of the blades. This may include factors, such as, inertial forces including centrifugal force, pressure, resonant frequencies of the blades, vibrations in the blades, vibratory stresses, temperature stresses, reseating of the blades, and load of the gas or other fluids. A prolonged increase in stress and fatigue over a period of time leads to defects and cracks in the blades. Furthermore, one or more of the cracks may widen or otherwise worsen with time to result in a liberation of a blade or a portion of the blade. The liberation of the blade may be hazardous for the device resulting in the failure of the device and significant cost. In addition, it may create an unsafe environment for people near the device and result in serious injuries.

Accordingly, it is highly desirable to develop a system and method that detects the health of rotor blades in real time. More particularly, it is desirable to develop a system and method that predicts cracks or fractures.

BRIEF DESCRIPTION

Briefly in accordance with one aspect of the technique, a method for monitoring the health of one or more blades is presented. The method includes the steps of generating a signal representative of delta times of arrival corresponding to the rotating blade, generating a reconstructed signal by decomposing the signal representative of the delta times of arrival utilizing a multi-resolution analysis technique, wherein the reconstructed signal is representative of at least one of static deflection and dynamic deflection in the rotating blade.

In accordance with an aspect of the present technique, a method for monitoring the health of a rotating blade is presented. The method includes the steps of generating a signal representative of filtered delta times of arrival corresponding to the rotating blade, selecting an appropriate wavelet based upon the signal representative of the filtered delta times TOAs and a decomposition level, decomposing the signal representative of the filtered delta times of arrival utilizing a multi-resolution analysis technique and the appropriate wavelet until the decomposition level is achieved to generate approximation coefficients and detailed coefficients; and generating a reconstructed signal utilizing the approximation coefficients, wherein the reconstructed signal is representative of static deflection in the rotating blade.

In accordance with one aspect, a system is presented. The system includes a processing subsystem that generates a signal representative of delta times of arrival corresponding to a rotating blade based upon actual times of arrival of the rotating blade, selects an appropriate wavelet based upon the signal representative of the delta times of arrival and a decomposition level, decomposes the signal representative of the delta times of arrival utilizing a multi-resolution analysis technique and the appropriate wavelet until the decomposition level is achieved to generate approximation coefficients and detailed coefficients, and generates a reconstructed signal utilizing the approximation coefficients, wherein the reconstructed signal is representative of static deflection in the rotating blade.

In accordance with another aspect, a non-transitory computer readable medium for a blade health monitoring system encoded with a program to instruct a computer is presented. The computer generates a signal representative of delta times of arrival corresponding to the plurality of rotating blades, selects an appropriate wavelet based upon the signal representative of the delta times of arrival and a decomposition level, decomposes the signal representative of the delta times of arrival utilizing a multi-resolution analysis technique and the appropriate wavelet until the decomposition level is achieved to generate approximation coefficients and detailed coefficients, and generates a reconstructed signal utilizing the approximation coefficients, wherein the reconstructed signal is representative of at least one of static deflection and dynamic deflection in the plurality of rotating blades.

DETAILED DESCRIPTION

As discussed in detail herein, embodiments of the present system and techniques evaluate the health of one or more rotating blades or airfoils. Hereinafter, the terms “airfoils,” “rotating blades” and “blades” will be used interchangeably. More particularly, the present system and techniques determine static deflection in the blades due to conditions, such as, one or more defects or cracks in the blades. As used herein, the term “static deflection” may be used to refer to a deflection in the position of a blade from the expected or original position of the blade. Certain embodiments of the present system and techniques also determine dynamic deflection corresponding to the blades. As used herein, the term, “dynamic deflection” may be used to refer to an amplitude of vibration of a blade over the mean position of the blade.

In operation, a time of arrival (TOA) of blades at a reference position after each rotation may vary from an expected TOA due to factors, such as, one or more cracks or defects in the blades. Hereinafter, the word “TOA” and the term “actual TOA” will be used interchangeably. The variation in the TOA of the blades is used to determine the static deflection and/or dynamic deflection in the rotating blades. As used herein, the term “expected TOA” may be used to refer to a predicted or expected TOA of a blade at a reference position after each rotation when there are no or insignificant defects or cracks in the blade and the blade is working properly, such as, in an ideal situation, load conditions are optimal, and the vibrations in the blade are minimal

FIG. 1is a diagrammatic illustration of a rotor blade health monitoring system10, in accordance with an embodiment of the present system. As shown inFIG. 1, the system10includes one or more rotating blades12. As shown by dotted lines14, the blades12may have static deflection or dynamic deflection. Therefore, the blades12are monitored by the system10to determine at least one of the static deflection and dynamic deflection in the blades12. As shown in the presently contemplated configuration, the system10includes one or more sensors16. The sensor16generates TOA signals18that are representative of actual TOAs of the blades12at a reference point for a determined time period. In one embodiment, the sensor16sense an arrival of the one or more blades12at the reference point to generate the TOA signals18. The reference point, for example, may be underneath the sensor16or adjacent to the sensor16. In an embodiment, each of the TOA signals18is sampled and/or measured for a particular time period and is used for determining the actual TOAs of the blades12. It may be noted that the delta TOA is measured in in units of time or degrees.

In one embodiment, the units of the delta TOA corresponding to each of the one or more blades may be converted in to units of mils For example, the delta TOA corresponding to each of the one or more blades that is in units of degrees may be converted in to units of mils using the following equation (1):

Δ⁢⁢ToAmils⁡(k)⁡(t)=2⁢π⁢⁢R×Δ⁢⁢To⁢⁢ADeg⁡(k)⁡(t)360(1)
where ΔToAmils(k)(t) is a delta TOA of a blade k at a t instant of time and the delta TOA is in units of mils, ΔToADeg(k)(t) is a delta TOA of the blade k at the t instant of time and the delta TOA is in units of degrees and, R is a radius of a blade from the center of a rotor of the blade. The radius R is in units of mils. In another embodiment, the delta TOA that is in units of seconds may be converted in to units of mils using the following equation (2):

Δ⁢⁢ToAmils⁡(k)⁡(t)=2⁢π⁢⁢R×N×Δ⁢⁢To⁢⁢Asec⁡(k)⁡(t)60(2)
where ΔToAmils(k)(t) is a delta TOA of a blade k at a t instant of time and the delta TOA is in units of mils, ΔToAsec(k)(t)) is a delta TOA of the blade k at the t instant of time and the delta TOA is in units of degrees and R is a radius of a blade from the center of a rotor of the blade. The radius R is in units of mils and N is the speed in rpm.

In one embodiment, the sensor16may sense an arrival of the leading edge of the blades12to generate the TOA signals18. In another embodiment, the sensor16may sense an arrival of the trailing edge of the one or more blades12to generate the signals18. The sensor16, for example, may be mounted adjacent to the one or more blades12on a stationary object in a position such that an arrival of each of the blades12may be sensed efficiently. In one embodiment, the sensor16is mounted on a casing (not shown) of the blades12. By way of a non-limiting example, the sensor16may be magnetic sensors, capacitive sensors, eddy current sensors, or the like. In a further example, the sensor16is a proximity sensor that is deployed on or proximate the casing (not shown) around the rotor. Such proximity sensor may be situated in the system10in a pre-existing design such that the present system10requires no additional sensor deployment.

As illustrated in the presently contemplated configuration, the TOA signals18are received by a processing subsystem22. The processing subsystem22determines actual TOAs of the blades12based upon the TOA signals18. Furthermore, the processing subsystem22determines at least one of static deflection and dynamic deflection in the blades12based upon the actual times of arrival (TOAs) of the blades12. The determination of the static deflection and/or dynamic deflection will be explained in greater detail with reference toFIGS. 2-4. In one embodiment, the processing subsystem22may have a data repository24that stores data, such as, static deflection, dynamic deflection, TOAs, delta TOAs, any intermediate data, or the like.

Referring now toFIG. 2, a flowchart representing an exemplary method200for determining static deflection and dynamic deflection in blades, in accordance with an embodiment of the invention, is depicted. For ease of understanding the exemplary method200will be explained with reference to a single blade. The blade, for example, may be one of the blades12(seeFIG. 1). The method200is depicted by steps202-216. At step202, actual TOAs may be determined by a processing subsystem, such as, the processing subsystem22(seeFIG. 1). As previously noted with reference toFIG. 1, the actual TOAs in one example is determined based upon the TOA signals18(seeFIG. 1).

At step204, delta TOAs corresponding to the blade are determined. A delta TOA corresponding to a blade, for example, may be a difference of an actual TOA corresponding to the blade that is received at step202and an expected TOA205corresponding to the blade. It may be noted that the delta TOA corresponding to the blade is representative of a variation in the actual TOA of the blade in comparison to the expected TOA205of the blade at a time instant. The delta TOA, for example, may be determined using the following equation (3):
ΔTOAk(t)=TOAact(k)(t)−TOAexp(k)(3)
where ΔTOAk(t) is a delta TOA corresponding to a blade k at a time instant t or a variation from the expected TOA corresponding to the blade k at the time instant t, TOAact(k)is an actual TOA corresponding to the blade k at the time instant t, and TOAexp(k)is an expected TOA corresponding to the blade k.FIG. 5shows exemplary delta times of arrival (TOAs) profile502wherein delta times of arrival are shown via. Y-axis, and speed of a device that includes the blades12are shown via. X-axis.

As used herein, the term “expected TOA” may be used to refer to an actual TOA of a blade at a reference position when there are no or insignificant defects, cracks, or other errors in the blade, and the blade is working in an operational state when effects of operational data on the actual TOA are minimal In one example, such expected TOA can be based on simulation data. In one embodiment, the expected TOA205corresponding to the blade may be determined by equating an actual TOA corresponding to the blade to the expected TOA205of the blade when a device that includes the blade has been recently commissioned, bought, or otherwise verified as healthy, including data from the manufacturing initialization. Such a determination assumes that since the device has been recently commissioned, bought, or otherwise been verified as healthy, all blades in the device are working in an ideal situation, the load conditions are optimal, and the vibrations in the blade are minimal In another embodiment, the expected TOA205may be determined by determining an average of actual times of arrival (TOAs) of the blades in the device. The device, for example, may include axial compressors, land based gas turbines, jet engines, high speed ship engines, small scale power stations, or the like.

In one embodiment, at step206, a signal may be generated that is representative of filtered delta TOAs208corresponding to the blade. The signal representative of the filtered delta TOAs, for example, may be generated by filtering the delta TOAs that have been determined at step204. For example, the delta TOAs may be filtered using one or more filtering techniques including a Savitzky-Golay technique, a median filtering technique, or combinations thereof. The delta TOA or the filtered delta TOA may comprise of static deflection and dynamic deflection. The static deflection may be considered as a slowly evolving long term trend while the dynamic deflection represents the short-term dynamics of the blade vibration. In other words, the static and the dynamic deflection may be considered as the low and high pass frequency components of the delta TOA or the filtered delta TOA, respectively. Wavelet analysis presents a powerful tool for separating the static deflection and dynamic deflection present in delta TOA or filtered delta TOA. Given the flexibility of choosing scales in wavelet decomposition, the required information may be compressed into one or more levels (indicated by the scale) in the multi-resolution analysis and this information alone may be reconstructed. For e.g., a low pass frequency component of a signal may be obtained through multi-resolution analysis performed to a high scale value. Further, a wavelet could also be used for extracting varying frequency (band-pass) information from a signal without the need for designing new filters.

Subsequently at step210, a reconstructed signal212in one example is generated by decomposing the signal that is representative of the filtered delta TOAs208. In another example, the reconstructed signal212is generated by decomposing the signal that is representative of the delta TOAs. The signal that is representative of filtered delta TOAs208or the delta TOAs may be decomposed into static deflection and dynamic deflection utilizing a multi-resolution analysis technique. The reconstructed signal212, for example, in one example is generated by the processing subsystem22(seeFIG. 1). It is noted that the reconstructed signal212is representative of static deflection in the blade.FIG. 5shows an exemplary static deflection profile504wherein the static deflection is shown via. Y-axis, and speed of a device that includes the blades12is shown via. X-axis. As shown inFIG. 5, the static deflection profile504is obtained by processing the delta TOA profile502. The generation of the reconstructed signal212utilizing the multi-resolution analysis technique will be explained in greater detail with reference toFIG. 3. Additionally, the multi-resolution analysis technique will be explained with reference toFIG. 4.

In one embodiment, at step214, dynamic deflection216in the blade is determined The dynamic deflection216in the blade in one example is determined by subtracting the signal representative of the filtered delta TOAs208from the reconstructed signal212. Particularly, the dynamic deflection216may be determined by subtracting a filtered delta TOA from respective static deflection. The dynamic deflection216, for example, is determined using the following equations (4) and (5):
Dynamic_Deflectionk(t)=FilteredΔTOAk(t)−Stat_defk(t)  (4)
Dynamic_Deflectionk(t)=ΔTOAk(t)−Stat_defk(t)  (5)
where Dynamic_Deflectionk(t) is a dynamic deflection of a blade k at a time instant t, FilteredΔTOAk(t) is a filtered delta TOA of the blade k at the time instant t, ΔTOAk(t) is a delta TOA of the blade k at the time instant t, and Stat_defk(t) is a static deflection in the blade k at the time instant t.FIG. 5shows an exemplary dynamic deflection profile506wherein the dynamic deflection is shown via. Y-axis, and speed of a device that includes the blades12is shown via X-axis. As shown inFIG. 5, the dynamic deflection profile506is obtained by processing the delta TOA profile502.

FIG. 3is a flowchart representing an exemplary method300for generating a reconstructed signal representative of at least one of static deflection and dynamic deflection in accordance with an embodiment of the present techniques. Particularly,FIG. 3explains step210inFIG. 2in greater detail. Furthermore, in one example,FIG. 3describes a method for generating a reconstructed signal318that is representative of dynamic deflection. At step302, where an appropriate wavelet based upon the signal that is representative of the filtered delta TOAs208is selected. In one embodiment, the appropriate wavelet may be selected by an operator. For example, the appropriate wavelet is an orthogonal wavelet or a bi-orthogonal wavelet, and has compact support. It is noted that whileFIG. 3shows selection of an appropriate wavelet based upon the signal that is representative of the filtered delta TOAs208, in one example, the appropriate wavelet is selected based upon a signal that is representative of the delta TOAs.

Subsequently at step304, a decomposition level is selected in one example. The decomposition level may be selected based upon the filtered delta TOAs208, signal to noise ratio of the signal representative of the filtered delta TOAs208, and the like. In certain embodiments, the decomposition level may be selected by an operator based on the length of delta TOA data. Subsequently at step306, according to one example, approximation coefficients308and detailed coefficients309are generated until the decomposition level is achieved. The approximation coefficients308and the detailed coefficients309may be generated utilizing the multi-resolution analysis technique. The generation of the approximation coefficients308and the detailed coefficients309will be explained in detail with reference toFIG. 4. At step310, the detailed coefficients309that have been generated at step306may be equated to zero. Furthermore, at step312, a signal may be reconstructed utilizing the approximation coefficients308. Consequent to the reconstruction of the signal at step312, the reconstructed signal212that is representative of static deflection is generated. In alternative embodiments, at step314, the approximation coefficients308may be equated to zero. Furthermore, at step316, a signal may be reconstructed utilizing the detailed coefficients309. Consequent to the reconstruction of the signal at step316, the reconstructed signal318that is representative of dynamic deflection is generated.

FIG. 4is a block diagram representing an exemplary multi-resolution analysis technique to generate the approximation coefficients308(seeFIG. 3) and detailed coefficients309, in accordance with an embodiment of the present techniques. Particularly,FIG. 4explains step306ofFIG. 3in greater detail. In the presently contemplated configuration, reference numeral402is representative of a signal x(n) that is representative of the filtered delta TOAs208, or delta TOAs. In one example, the signal x(n)402is decomposed in to low frequencies and high frequencies utilizing a low pass filter g(n)404and a high pass filter h(n)406until an Nthdecomposition level is achieved. As previously noted with reference toFIG. 3, the decomposition level M is selected at step304. In one embodiment, the decomposition level may be selected utilizing the following equation (6):

M=[[log⁡(N/P)log⁢⁢2]](6)
where N is the length of filtered delta TOAs or delta TOAs, P is the length of filters g[n] and h[n] and M is a decomposition level. For e.g., if the length of delta TOA data is 20000, and length of the filters g(n) and h(n) is 8, then the value of M is determined as 11. Therefore, in this example, the value of decomposition level is 11. In another embodiment, the decomposition level may be selected from a range of M−4 to M, where M is determined utilizing equation (6). For instance in the above example, the value of decomposition level may vary from 7 to 11. It is noted that the low pass filter g(n)404and the high pass filter h(n)406are formed based upon the appropriate wavelet that is selected at step302inFIG. 3.

As shown inFIG. 4, in a first decomposition level, the signal x(n)402is decomposed by passing the signal x(n)402through the low pass filter g(n)404and high pass filter h(n)406to generate coefficients408and410, respectively. Furthermore, the coefficients408,410are down sampled412to generate first level approximation coefficients A1and first level detailed coefficients D1, respectively. Subsequently, in a second decomposition level, the approximation coefficients A1are passed through the low pass filter g(n)404and the high pass filter h(n)406to generate coefficients414,416, respectively. The coefficients414,416are down sampled412to generate second level approximation coefficients A2and second level detailed coefficients D2, respectively. Similarly, in Nthdecomposition level (N−1)thapproximation coefficients A(N−1) that are generated in (N−1)thdecomposition level are passed through the low pass filter g(n)404followed by downsampling412to generate Nthlevel approximation coefficients AN. Additionally, in Nthdecomposition level, the (N−1)thlevel approximation coefficients A(N−1) are passed through the high pass filter h(n)406followed by downsampling412to generate Nthlevel detailed coefficients D(N). In the presently contemplated configuration, the (N−1)thdecomposition level is a second decomposition level and the Nthdecomposition level is a third decomposition level. In one example, the approximation coefficients A(N) are the approximation coefficients308, and the detailed coefficients D(N) are the detailed coefficients309.

Various embodiments described herein provide a tangible and non-transitory machine-readable medium or media having instructions recorded thereon for a processor or computer to operate a system for monitoring health of rotor blades, and perform an embodiment of a method described herein. The medium or media may be any type of CD-ROM, DVD, floppy disk, hard disk, optical disk, flash RAM drive, or other type of computer-readable medium or a combination thereof.

It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the various embodiments without departing from their scope. While the dimensions and types of materials described herein are intended to define the parameters of the various embodiments, they are by no means limiting and are merely exemplary. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the various embodiments should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. Further, the limitations of the following claims are not written in means-plus-function format and are not intended to be interpreted based on 35 U.S.C. §112, sixth paragraph, unless and until such claim limitations expressly use the phrase “means for” followed by a statement of function void of further structure. It is to be understood that not necessarily all such objects or advantages described above may be achieved in accordance with any particular embodiment. Thus, for example, those skilled in the art will recognize that the systems and techniques described herein may be embodied or carried out in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other objects or advantages as may be taught or suggested herein.