Sign detector, sign detection method and sign detection program

Abstract

To preferably detect a sign of combustion oscillation having various features.SOLUTION: A sign detector for detecting a sign of oscillation generated in a combustor of a gas turbine. The sign detector detects a sign on the basis of a first index and a second index. The first index is calculated as an index indicating at least one of regression property between multiple multi-dimensional vectors including multiple pieces of data included in time series data acquired by a first sensor installed on the combustor, as vector components, and intermittent property between the multiple multi-dimensional vectors. The second index is calculated as an index indicating synchronism between multiple pieces of time series data acquired from a second sensor and a third sensor which are installed at different positions on the combustor.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to a sign detection device, a sign detection method, and a sign detection program. [Background technology]

[0002] In gas turbines driven by combustion gases produced by burning fuel, unstable vibrations that tend to suddenly change (sudden vibrations) can occur in the combustor. Such sudden vibrations reach a limit cycle in a short time after an increase in vibration occurs. When the sudden vibrations reach the limit cycle, they can cause a trip or place a large load on the equipment.

[0003] Therefore, it is desirable to avoid such sudden vibrations at an early stage. However, in the case of sudden vibrations, the increase in vibration until the limit cycle is reached occurs in a short time, so there is a risk that the sudden vibrations cannot be avoided by control after the vibration increase is actually detected. Therefore, in order to avoid sudden vibrations, it is necessary to detect a precursor of the sudden vibration well before its occurrence. For example, Patent Document 1 discloses a technology for detecting a precursor of combustion vibrations occurring in a coupled manner in multiple combustors, based on a parameter indicating a correlation between physical quantities acquired from sensors provided in each of the multiple combustors of a gas turbine. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-162397 Summary of the Invention [Problem to be solved by the invention]

[0005] In the above-mentioned Patent Document 1, the detection target is a sign of combustion oscillation having a frequency band of several hundreds of Hz that occurs in a plurality of combustors, and it is not possible to detect a sign of combustion oscillation in other frequency bands that occurs in only a single combustor. In recent years, the use of fuels with high combustion speeds, such as hydrogen gas, has been considered for use in gas turbines. Hydrogen gas has a higher combustion speed than natural gas and other fuels that have been used conventionally, and therefore the length of the flame formed in the combustor becomes shorter, making it easier for combustion oscillations in higher frequency bands to occur.

[0006] At least one embodiment of the present disclosure has been developed in consideration of the above-mentioned circumstances, and aims to provide a precursor detection device, a precursor detection method, and a precursor detection program that are capable of preferably detecting precursors of combustion oscillations having various characteristics. [Means for solving the problem]

[0007] In order to solve the above problem, the sign detection device according to at least one embodiment of the present disclosure is A symptom detection device for detecting a symptom of vibration occurring in a combustor of a gas turbine, comprising: a first index calculation unit that calculates a first index indicating at least one of a recurrence between a plurality of multidimensional vectors having a plurality of data included in time-series data acquired from a first sensor installed in the combustor as vector components, or an intermittency between the plurality of multidimensional vectors; a second index calculation unit for calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; a sign detection unit for detecting the sign based on the first indicator and the second indicator; Equipped with.

[0008] In order to solve the above problem, a sign detection method according to at least one embodiment of the present disclosure includes: A method for detecting a sign of vibration occurring in a combustor of a gas turbine, comprising: calculating a first index indicating at least one of a recurrence between a plurality of multidimensional vectors having a plurality of data included in time-series data acquired from a first sensor installed in the combustor as vector components, or an intermittency between the plurality of multidimensional vectors; calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; detecting the sign based on the first indicator and the second indicator; Equipped with.

[0009] In order to solve the above problem, at least one embodiment of a symptom detection program according to the present disclosure includes: 1. A program for detecting a sign of vibration occurring in a combustor of a gas turbine, comprising: A computer device includes: calculating a first index indicating at least one of a recurrence between a plurality of multidimensional vectors having a plurality of data included in time-series data acquired from a first sensor installed in the combustor as vector components, or an intermittency between the plurality of multidimensional vectors; calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; detecting the sign based on the first indicator and the second indicator; It is possible to execute the following. Effect of the Invention

[0010] According to at least one embodiment of the present disclosure, it is possible to provide a sign detection device, a sign detection method, and a sign detection program capable of suitably detecting signs of combustion oscillations having various characteristics. [Brief description of the drawings]

[0011] [Figure 1] 1 is a schematic cross-sectional view of a gas turbine according to an embodiment of the present invention taken along a turbine shaft in the vicinity of a combustor. [Diagram 2]2 is a cross-sectional view taken along line AA in FIG. 1. [Diagram 3] 1 is a block diagram illustrating the configuration of a sign detection device according to an embodiment. [Figure 4] This is an example of a multidimensional vector in D-dimensional topological space. [Diagram 5] FIG. 5 is an example of a recurrence plot corresponding to FIG. 4. [Figure 6] FIG. 13 is a diagram showing the correspondence between the distribution of recurrence points in a recurrence plot and the index De. [Figure 7] FIG. 13 is a diagram showing the correspondence between the distribution of recurrence points in a recurrence plot and the index La. [Figure 8] 1 is an example of the distribution of recurrence points in a joint recurrence plot. [Figure 9] FIG. 1 is an explanatory diagram for determining a cross-recurrence plot from the distance between data points of two time series data in a topological space. [Figure 10] A conceptual diagram of the order parameter K. [Figure 11A] This shows past data of physical quantities obtained from the first sensor when combustion oscillation having a relatively low frequency band around 3000 Hz was observed. [Figure 11B] FIG. 11B is a recurrence plot corresponding to FIG. 11A. [Figure 11C] 11B is a diagram showing an example of a clustering result for N pieces of data defined by average values ​​μDe and μLa corresponding to the indices De and La, respectively, corresponding to FIG. 11A. FIG. [Figure 12] FIG. 13 is a diagram showing an example of the results of clustering past data of the average value μCD corresponding to the index CD, which is the second index, when combustion oscillation having a relatively high frequency band in the vicinity of 4500 Hz is observed. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, some embodiments will be described with reference to the accompanying drawings. However, the dimensions, materials, shapes, relative arrangements, etc. of the configurations described as the embodiments or shown in the drawings are merely illustrative examples and are not intended to limit the scope of the invention.

[0013] First, a gas turbine 1 that is a detection target of a sign detection device according to at least one embodiment of the present disclosure will be described with reference to Fig. 1. Fig. 1 is a schematic cross-sectional view taken along a turbine shaft in the vicinity of a combustor 8 included in the gas turbine 1 according to one embodiment, and Fig. 2 is a cross-sectional view taken along the line AA in Fig. 1.

[0014] The gas turbine 1 includes a compressor 2, a combustor 4, a stator vane 6, and a rotor blade 8. The combustors 4 are can-type combustors, and a plurality of them are arranged along the circumferential direction of the turbine shaft. The plurality of combustors 4 have the same configuration, and FIG. 1 shows an arbitrary one of the plurality of combustors 4 as a representative example. Each combustor 4 includes a fuel nozzle 10 and a transition piece 12.

[0015] In addition, at least one sensor is installed in the combustor 4 of the gas turbine 1 to detect a sign by a sign detection device 100 described later. In FIG. 1, three sensors (a first sensor 20a, a second sensor 20b, and a third sensor 20c) are installed in the transition piece 12 of the combustor 4 as the sensors. In this embodiment, for convenience of explanation, the first sensor 20a, the second sensor 20b, and the third sensor 20c are installed at different positions, but it is sufficient that at least the second sensor 20b and the third sensor 20c are installed at different positions (i.e., the first sensor 20a may be arranged at the same position as the second sensor 20b or the third sensor 20c). In addition, the second sensor 20b and the third sensor 20c may be arranged along the circumferential direction or along the axial direction with respect to the central axis of the transition piece 12 of the combustor 4 as shown in FIG. 2. The second sensor 20b or the third sensor 20c may also function as the first sensor 20a.

[0016] These sensors (first sensor 20a, second sensor 20b, and third sensor 20c) can measure a physical quantity at each position. The physical quantity that the sensors can measure is not limited as long as it is related to the occurrence of combustion oscillation, and is, for example, at least one of pressure, strain, acceleration, velocity, and displacement.

[0017] Next, a description will be given of a sign detection device 100 for detecting signs of combustion oscillation in the gas turbine 1 having the above configuration. The sign detection device 100 is, for example, a computer equipped with a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), etc. In the sign detection device 100, a processor (CPU) executes a program stored in the memory (RAM or ROM), thereby realizing various functions described below.

[0018] 3 is a block diagram of a sign detection device 100 according to an embodiment. The sign detection device 100 includes a time-series data acquisition unit 110, an index calculation unit 120, a sign detection unit 130, and an output unit 140.

[0019] The time-series data acquiring unit 110 is configured to acquire time-series data of physical quantities from each sensor (the first sensor 20a, the second sensor 20b, and the third sensor 20c). The time-series data includes a plurality of data sampled at a plurality of timings (i.e., a predetermined sampling period) in a unit time in the most recent past.

[0020] The index calculation unit 120 is configured to calculate an index for determining the presence or absence of a sign of combustion oscillation, based on the time-series data acquired by the time-series data acquisition unit 110. In this embodiment, the index calculation unit 120 is capable of calculating a first index I1 and a second index I2 as the indexes, and includes a first index calculation unit 122 for calculating the first index I1 and a second index calculation unit 124 for calculating the second index I2 (specific methods of calculating the first index I1 and the second index I2 will be described later).

[0021] The sign detection unit 130 is configured to detect a sign of combustion oscillation based on the index calculated by the index calculation unit 120. The sign detection unit 130 can appropriately detect a sign of combustion oscillation having different characteristics by handling both the first index I1 calculated by the first index calculation unit 122 and the second index I2 calculated by the second index calculation unit 124 as the index calculated by the index calculation unit 120. That is, the combustion oscillation to be detected may have different characteristics depending on the frequency band, for example, and there are some that are highly sensitive to the first index I1 and low sensitive to the second index I2, and some that are low sensitive to the first index I1 and high sensitive to the second index I2. Therefore, if an attempt is made to detect a sign based on only one of the first index I1 or the second index I2, there is a risk that the combustion oscillation will be overlooked depending on its characteristics. Therefore, the sign detection unit 130 can appropriately detect combustion oscillations with various characteristics by performing sign detection based on both the first index I1 and the second index I2.

[0022] The output unit 140 is configured to perform various outputs based on the detection result by the sign detection unit 130. The contents output by the output unit 140 may include, for example, related data such as time series data acquired by the time series data acquisition unit 110 and indexes calculated by the index calculation unit 120, in addition to various data indicating the signs detected by the sign detection unit 130. Furthermore, when a sign is detected by the sign detection unit 130, the output unit 140 may output a display or sound indicating an alarm to notify the operator of the detection, or may automatically output a control signal to another control device to avoid combustion oscillation.

[0023] Next, a calculation method of the index in the index calculation unit 120 will be specifically described. First, in the first index calculation unit 122 of the index calculation unit 120, a first index I1 is calculated as an index. The first index I1 is calculated based on time series data acquired from the first sensor 20a installed in the combustor 4. That is, the first index I1 is an index that can be calculated from data acquired from a single sensor. The first index I1 is calculated as an index related to at least one of the recurrence between a plurality of multidimensional vectors, in which a plurality of data included in the time series data acquired from the first sensor 20a are vector components, and the intermittency between a plurality of multidimensional vectors. Recurrence means that the similarity of data at two different times in a time series signal continues over time (for example, at time t i and time (t i +Δt), time (t i+1 ) and time (t i+1 +Δt), time (t i+2 ) and time (t i+2 +Δt), and intermittency means that in a time series signal, similarity continues between a certain time and multiple consecutive times (for example, between time t i and time (t i +Δt), time ti and time (t i+1 +Δt), time t i and time (t i+2 +Δt) the similarity continues.

[0024] Specifically, the time series data x(t i ) (i=1, 2, . . . , N) includes N pieces of measurement data acquired at each predetermined sampling period. The first index calculation unit 122 calculates the time series data x(t i ) (i = 1, 2, , N), the delay time τ and the multidimensional vector X(t i Specifically, we define a multidimensional vector X(t i ) is defined by the following formula: X(t i )={x(ti ),x(t i +1),···,x(t i +(D-1)τ)} (1)

[0025] Figure 4 shows the multidimensional vector X(t i ), and FIG. 5 is an example of a recurrence plot corresponding to FIG. 4. The D-dimensional topological space is a multidimensional space whose axes are represented by x(t), x(t+τ), x(t+2τ), and FIG. 4 illustrates an example of a case where D=3 that can be represented on paper. The first index calculation unit 122 calculates the recurrence plot corresponding to any two times (t i ,t j ) the Euclidean distance X(t i )-X(t j ) and the threshold ε, a recurrence plot is made based on the following formula: TIFF2025034371000002.tif12170The recurrence plot obtained in this way shows that the Euclidean distance X(t i )-X(t j ) below a threshold ε, recurrence points with similar behavior are highlighted.

[0026] The first index calculation unit 122 can calculate an index De (Determinism), which is a ratio of all regression points constituting the diagonal line structure included in the recurrence plot, as an example of the first index I1 related to the recurrence. Here, FIG. 6 is a diagram showing the correspondence relationship between the distribution of the regression points in the recurrence plot and the index De. In FIG. 6, the length of a representative diagonal line structure is indicated by the symbol I. When the index De is low, the number and length of the diagonal line structures included in the distribution of the regression points in the recurrence plot are small, and the distribution of the regression points becomes close to random. On the other hand, when the index De is high, the number and length of the diagonal line structures included in the distribution of the regression points in the recurrence plot become large. In this way, the recurrence means that similar behavior occurs repeatedly at a time every predetermined period, and can be treated as an index related to combustion oscillation. Such an index De related to the recurrence can be quantitatively defined, for example, by the following equation. The index De defined by the above equation (3) can take a value in the range of 0≦De≦1, and the higher the periodicity, the closer to 1 it indicates a value.

[0027] The first index calculation unit 122 can calculate an index La (Laminarity), which is a ratio of all regression points constituting a vertical structure or a horizontal structure included in the recurrence plot, as an example of the first index I1 related to intermittency. Here, FIG. 7 is a diagram showing the correspondence relationship between the distribution of regression points in the recurrence plot and the index La. In FIG. 7, the length of a representative vertical structure is indicated by the symbol v. When the index La is low, the number and length of vertical structures included in the distribution of regression points in the recurrence plot are small, and the distribution of regression points becomes close to random. On the other hand, when the index La is high, the number and length of vertical structures included in the distribution of regression points in the recurrence plot become large. In this way, intermittency means that similar behavior occurs repeatedly between successive times, and can be treated as an index related to combustion oscillation. Such an index La related to intermittency can be quantitatively defined, for example, by the following equation. The index La defined by the above equation (4) can take a value in the range of 0≦La≦1, and the smaller the frequency of the periodic function, the closer to 1 it indicates a value.

[0028] Next, a second index calculation unit 124 of the index calculation unit 120 calculates a second index I2 as an index. The second index I2 is calculated based on the synchronicity of multiple time-series data acquired from the second sensor 20b and the third sensor 20c, which are respectively installed at different positions in the combustor 4. That is, the second index I2 is an index that can be calculated from data acquired from multiple sensors arranged at different positions.

[0029] The second index calculation unit 124 can calculate an index De (Determinism) which is a ratio of all recurrence points constituting the diagonal line structure included in the joint recurrence plot as an example of the second index I2 related to synchrony. The joint recurrence plot is an extension of the recurrence plot obtained based on the time series data acquired from the single first sensor 20a described above in order to apply it to a plurality of time series data (multivariate time series data) acquired from the second sensor 20b and the third sensor 20c.

[0030] Specifically, the time series data x(t i ) (i=1, 2, . . . , N), and the time series data y(t i ) (i=1,2,...,N) consider the recurrence of the orbits in each phase space separately, and if both recur simultaneously, plot the return points in black at the corresponding coordinates, otherwise plot the return points in white. Specifically, the joint recurrence plot JR(i,j) is defined by the following equation (where i,j=1,2,...,Np, Np is the number of data points in the phase space): TIFF2025034371000005.tif5170 However, X(t i )={x(t i ),x(t i +τ),···,x(ti +(D-1)τ)}, Y(ti) = {y(t i ),y(t i +τ),···,y(t i +(D-1)τ)}, where D is the embedding dimension, τ is the delay time, ε is the threshold, and Θ is the Heaviside function.

[0031] Here, Fig. 8 is an example of a distribution of return points in a joint recurrence plot. As shown in Fig. 8, the joint recurrence plot JR(i,j) is a distribution of the time series data X(t i )={x(t i ),x(t i +τ),···,x(t i +(D-1)τ)}, and the time series data Y(t i )={y(t i ),y(t i +τ),···,y(t i +(D−1)τ)}, the intersection points with the recurrence plot Ry(i,j) created based on

[0032] The second index calculation unit 124 can calculate the degree of determinacy JD, which is the ratio of the regression points constituting the oblique line structure to all regression points, for such a joint recurrence plot JR(i,j) as the second index I2. The index JD for the joint recurrence plot JR(i,j) is quantitatively defined by the following equation. TIFF2025034371000006.tif12170

[0033] Furthermore, the second index calculation unit 124 can calculate an index De (Determinism) which is a ratio of all recurrence points constituting the diagonal line structure included in the cross recurrence plot as an example of the second index I2 related to synchrony. The cross recurrence plot is an extension of the recurrence plot obtained based on the time series data acquired from the single first sensor 20a described above in order to apply it to a plurality of time series data (multivariate time series data) acquired from the second sensor 20b and the third sensor 20c.

[0034] FIG. 9 is an explanatory diagram for determining a cross-recurrence plot from the distance between data points of two time series data in a phase space. Specifically, the time series data x(t i ) (i=1, 2, . . . , N), and the time series data y(t i ) (i = 1, 2, . . . , N) into a common topological space, and the time series data x(t i ) and y(t i ) are close (smaller than the threshold ε), the return points are plotted in black at the corresponding coordinates, otherwise the return points are plotted in white. Specifically, the cross recurrence plot CR(i,j) is defined by the following equation (where i,j=1,2,...,Np, where Np is the number of data points in the phase space): TIFF2025034371000007.tif6170 However, X(t i )={x(t i ),x(t i +τ),···,x(t i +(D-1)τ)}, Y(t i )={y(t i ),y(t i +τ),···,y(t i +(D-1)τ)}, where D is the embedding dimension, τ is the delay time, ε is the threshold, and Θ is the Heaviside function.

[0035] In a cross recurrence plot, when the distance between data points in the phase space becomes close for a long period of time, a diagonal line structure is formed. Therefore, the second index calculation unit 124 can calculate the degree of determinacy CD, which is the ratio of the recurrence points constituting the diagonal line structure to the total recurrence points, for such a cross recurrence plot CR(i,j) as the second index I2. The index CD in the cross recurrence plot CR(i,j) is quantitatively defined by the following equation. TIFF2025034371000008.tif12170

[0036] The second index calculation unit 124 can calculate an order parameter K (Kuramoto order parameter) obtained from a plurality of time series data as an example of the second index I2 related to synchrony. The order parameter K is calculated by multiplying the time series data x(t i ) (i=1, 2, . . . , N), and the time series data y(t i ) (i = 1, 2, , N) to the following equation: TIFF2025034371000009.tif8170 where θj(t) indicates the phase of the j-th oscillator at time t. The order parameter K indicates the distance between the origin and the coordinates of the center of gravity of the oscillators distributed on a unit circle, and can take values ​​in the range of 0≦K≦1. Figure 10 is a conceptual diagram of the order parameter K. The closer the order parameter K is to a synchronous state, the closer it is to 1.

[0037] Next, a method for determining the presence or absence of a sign in the sign detection unit 130 will be specifically described. The sign detection unit 130 detects a sign based on both the first index I1 calculated by the first index calculation unit 122 and the second index I2 calculated by the second index calculation unit 124. As described above, the first index I1 and the second index I2 are indexes that can determine the presence or absence of a sign from different perspectives, and therefore, by performing sign detection based on both of these indexes, the sign detection unit 130 can preferably detect a sign that would be missed by using only one of the indexes.

[0038] The sign detection unit 122 identifies a classification corresponding to the sign by applying a classification model to past data of the first index I1 or the second index I2, and detects the sign based on whether the first index I1 calculated by the first index calculation unit 122 and the second index I2 calculated by the second index calculation unit 124 belong to the classification. The classification model used in the sign detection unit 13 can be constructed, for example, by applying clustering to past data. In this embodiment, a case where a classification model is constructed by K-means, which is a type of clustering, is illustrated, but the construction method of the classification model is not limited to this.

[0039] In the K-means method, N pieces of data x i (i=1,2,...,N)=[μDe(t i ),μLa(t i )], K cluster centroids mk (k = 1, 2, , K) are assumed, where μDe is the average calculated from multiple indices De, and μLa is the average calculated from multiple indices La.

[0040] Next, the N pieces of data are classified into K clusters so that the objective function J defined by the following equation is minimized. Then, a new center of gravity mk is calculated for each classified data point, and the process is repeated until there is no change in the center of gravity mk, thereby identifying classifications corresponding to the K clusters. TIFF2025034371000010.tif6170Note that rik is an indicator function and is expressed by the following equation. TIFF2025034371000011.tif10170

[0041] FIG. 11A shows past data of a physical quantity (pressure value) obtained from the first sensor 20a when combustion oscillation having a relatively low frequency band around 3000 Hz was observed, FIG. 11B is a recurrence plot corresponding to FIG. 11A, and FIG. 11C shows an example of a clustering result for N pieces of data defined by average values ​​μDe and μLa corresponding to the indicators De and La, respectively, corresponding to 11A.

[0042] In this example, by setting K = 5, the results are clustered into five categories defined by the magnitude of the possibility of combustion oscillation occurring. Category 1 indicates that the combustion state is stable because there is no possibility of combustion oscillation occurring. On the other hand, category 5 is the category with the highest possibility of combustion oscillation occurring, and indicates that combustion oscillation is actually occurring. Categories 2 to 4 are between categories 1 and 5, and are categories that indicate a state in which combustion oscillation is not actually occurring but the possibility of combustion oscillation occurring is increasing, in other words, they indicate signs of combustion oscillation.

[0043] At time 1, as shown in FIG. 11A, the data belongs to Class 1. At this time, as shown in FIG. 11B, the corresponding recurrence plot shows that the data exists randomly as isolated points, and the diagonal line structures indicating signs of combustion oscillation are hardly observed. At time t2, as shown in FIG. 11A, the data belongs to Class 4. At this time, as shown in FIG. 11B, the corresponding recurrence plot shows that the diagonal line structures indicating signs of combustion oscillation are increasing compared to time t1 (particularly, diagonal line structures partially appear in the area surrounded by the dashed line). At time t3, as shown in FIG. 11A, the data belongs to Class 5. At this time, as shown in FIG. 11B, the corresponding recurrence plot shows that many diagonal line structures overall, and combustion oscillation is actually occurring.

[0044] From the results of clustering analysis of past data using the indices De and La, which are the first indices I, it is possible to appropriately observe the occurrence of combustion oscillations having a frequency band near 3000 Hz. On the other hand, although not shown in Figs. 11A to 11C, it was not possible to observe any change due to the combustion oscillations from the second index I2 calculated based on the past data shown in Fig. 11A. From this, it was found that the second index I2 has poor sensitivity to combustion oscillations having a relatively low frequency band near 3000 Hz, and the first index I1 has excellent sensitivity.

[0045] According to the inventor's verification, combustion oscillations having a frequency band near 3000 Hz have not only a specific central frequency, but also a behavior in which the oscillation frequency transitions to surrounding frequencies over time (in other words, a behavior in which the oscillation modes switch rapidly between multiple oscillation modes having close oscillation frequencies), and therefore it appears that the second index I2 regarding synchronicity has poor sensitivity and the first index I1 regarding recurrence and intermittency has excellent sensitivity.

[0046] From the above results, it has been found that when the combustion oscillation to be detected has a relatively low frequency band, it is effective for the sign detection unit 130 to detect the presence or absence of an indication based on the first index I1. In this case, the sign detection unit 130 identifies a classification corresponding to the indication stage by performing clustering analysis on past data based on the indexes De and La, which are the first index I1, as exemplified in Figs. 11A to 11C. Then, based on the time-series data acquired from the actual machine by the time-series data acquisition unit 110, it is possible to determine the presence or absence of an indication of combustion oscillation based on whether the first index I1 calculated by the first index calculation unit 122 belongs to the classification.

[0047] Next, FIG. 12 is a diagram showing an example of the result of clustering past data of the average value μCD corresponding to the index CD (see the above formula (8)), which is the second index I2, when a combustion oscillation having a relatively high frequency band in the vicinity of 4500 Hz is observed. In this example, by setting K=3, the result of clustering into three categories defined by the degree of possibility of indicating combustion oscillation is shown. Category 1 indicates that the combustion state is stable because there is no possibility of combustion oscillation. On the other hand, Category 3 is the category with the highest possibility of combustion oscillation and indicates that combustion oscillation is actually occurring. Category 2 is between Category 1 and Category 3, and indicates a state in which combustion oscillation is not actually occurring but the possibility of combustion oscillation occurring is increasing, that is, a category that indicates a sign of combustion oscillation. In addition, FIG. 12 illustrates a case in which the index CD is calculated based on pressure from other sensors 20d to 20f in addition to the first sensor 20a, second sensor 20b, and third sensor 20c described above as multiple sensors capable of acquiring a physical quantity (pressure).

[0048] As shown in FIG. 12, at time t4, the data belongs to Classification 1. At this time, the index CD calculated from the cross recurrence plot indicates that the correlation between the time series data obtained from each sensor is small, meaning that no combustion oscillation is occurring. At time t5, the data belongs to Classification 2. At this time, the index CD calculated from the cross recurrence plot indicates that the correlation between the time series data obtained from each sensor increases, meaning that there are signs of combustion oscillation. At time t6, the data belongs to Classification 3. At this time, the index CD calculated from the cross recurrence plot indicates that the correlation between the time series data obtained from each sensor increases, meaning that combustion oscillation is actually occurring.

[0049] From the results of clustering analysis of past data using the index CD, which is the second index I2, the occurrence of combustion oscillations having a frequency band near 4500 Hz is observed. On the other hand, although not shown in Fig. 12, the first index I1 calculated based on past data could not observe any changes due to the combustion oscillations. From this, it was found that the first index I1 has poor sensitivity to combustion oscillations having a relatively high frequency band near 4500 Hz, and the second index I2 has excellent sensitivity.

[0050] According to the inventor's verification, combustion oscillations having a frequency band near 3000 Hz have not only a specific central frequency, but also a behavior in which the oscillation frequency transitions to surrounding frequencies over time (in other words, a behavior in which the oscillation modes switch rapidly between multiple oscillation modes having close oscillation frequencies), and therefore it appears that the second index I2 regarding synchronicity has poor sensitivity and the first index I1 regarding recurrence and intermittency has excellent sensitivity.

[0051] From the above results, it has been found that when the combustion oscillation to be detected has a relatively high frequency band, it is effective for the sign detection unit 130 to detect the presence or absence of an indication based on the second index I2. In this case, the sign detection unit 130 identifies a classification corresponding to the indication stage by performing clustering analysis on past data based on the index CD which is the second index I2, as exemplified in Fig. 12. Then, based on the time-series data acquired from the actual machine by the time-series data acquisition unit 110, it is possible to determine the presence or absence of an indication of combustion oscillation based on whether the second index I2 calculated by the second index calculation unit 124 belongs to the classification.

[0052] As described above, the sign detection unit 130 performs sign detection based on both the first index I1 calculated based on the time series data acquired from the first sensor 20a and the second index I2 calculated based on a plurality of time series data acquired from the second sensor 20b and the third sensor 20c. In the former, the first index I1 indicating at least one of the recurrence and intermittency of each data included in the time series data acquired by a single sensor is used as an index for detecting a sign. In the latter, the second index I2 indicating the synchronism between a plurality of time series data acquired by a plurality of sensors arranged at different positions is used as an index for detecting a sign. In this way, by performing sign detection based on the first index I1 and the second index I2 that can evaluate the combustion oscillation from different viewpoints, it is possible to detect combustion oscillations having various oscillation characteristics at an early stage of a sign.

[0053] In addition, within the scope of the present disclosure, the components in the above-described embodiments may be replaced with well-known components as appropriate, and the above-described embodiments may be combined as appropriate.

[0054] The contents described in each of the above embodiments can be understood, for example, as follows.

[0055] (1) According to one aspect, a sign detection device includes: A symptom detection device for detecting a symptom of vibration occurring in a combustor of a gas turbine, comprising: a first index calculation unit that calculates a first index indicating at least one of a recurrence between a plurality of multidimensional vectors having a plurality of data included in time-series data acquired from a first sensor installed in the combustor as vector components, or an intermittency between the plurality of multidimensional vectors; a second index calculation unit for calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; a sign detection unit for detecting the sign based on the first indicator and the second indicator; Equipped with.

[0056] According to the above aspect (1), a sign is detected based on both a first index calculated based on time series data acquired from a first sensor and a second index calculated based on a plurality of time series data acquired from a second sensor and a third sensor. In the former, a first index indicating at least one of recurrence and intermittency of each data included in the time series data acquired by a single sensor is used as an index for detecting a sign. In the latter, a second index indicating synchronicity between a plurality of time series data acquired by a plurality of sensors arranged at different positions is used as an index for detecting a sign. In this way, by performing sign detection based on the first index and the second index that can evaluate combustion oscillation from different viewpoints, combustion oscillation having various oscillation characteristics can be detected early at the sign stage.

[0057] (2) In another embodiment, in the above embodiment (1), The sign detection unit identifies a classification corresponding to the sign by applying a classification model to past data of the first index or the second index, and detects the sign based on whether the first index or the second index belongs to the classification.

[0058] According to the above aspect (2), the presence or absence of a warning sign can be detected based on whether or not each data included in the time series data acquired from the sensor belongs to a classification identified by applying a classification model to past data of the first index or the second index.

[0059] (3) In another embodiment, in the above embodiment (1) or (2), The first index calculation unit calculates the first index indicating the recurrence as a parameter indicating a ratio of a recurrence point constituting a diagonal line structure included in a recurrence plot corresponding to the plurality of multidimensional vectors to all recurrence points.

[0060] According to the above aspect (3), a recurrence plot corresponding to a plurality of multidimensional vectors having a plurality of data included in the time series data acquired from the first sensor as vector components is created. A first index indicating recurrence is quantitatively calculated as a parameter (e.g., determinism of the recurrence plot) indicating the ratio of the recurrence points constituting the diagonal line structure included in the recurrence plot to all the recurrence points.

[0061] (4) In another embodiment, in any one of the above (1) to (3), The first index calculation unit calculates the first index indicating the intermittency as a parameter indicating a ratio of a regression point constituting a vertical structure or a horizontal structure included in a recurrence plot corresponding to the plurality of multidimensional vectors to all regression points.

[0062] According to the above aspect (4), a recurrence plot corresponding to a plurality of multidimensional vectors having a plurality of data included in the time series data acquired from the first sensor as vector components is created. A first index indicating intermittency is quantitatively calculated as a parameter (e.g., laminarity of the recurrence plot) indicating the ratio of a recurrence point constituting a vertical structure or a horizontal structure included in the recurrence plot to all recurrence points.

[0063] (5) In another embodiment, in any one of the above (1) to (4), The second index calculation unit calculates the second index as at least one of an order parameter obtained from the plurality of time series data, a parameter indicating a ratio of regression points forming a diagonal structure included in a joint recurrence plot obtained from the plurality of time series data to all regression points, or a parameter indicating a ratio of regression points forming a diagonal structure included in a cross recurrence plot obtained from the plurality of time series data to all regression points.

[0064] According to the above aspect (5), the second index is quantitatively calculated as an order parameter obtained from a plurality of time series data acquired from the second sensor and the third sensor, and a parameter indicating the ratio of the regression points constituting the diagonal line structure included in the joint recurrence plot or the cross recurrence plot to all regression points (e.g., Determinism of the joint recurrence plot or the cross recurrence plot).

[0065] (6) In another embodiment, in any one of the above (1) to (5), The second sensor and the third sensor are disposed along a circumferential direction with respect to a central axis of the combustor.

[0066] According to the above aspect (6), the second sensor and the third sensor for acquiring time series data used in calculation of the second index are arranged along the circumferential direction with respect to the central axis of the combustor, thereby making it possible to acquire time series data at positions different from each other.

[0067] (7) A sign detection method according to one aspect includes the steps of: A method for detecting a sign of vibration occurring in a combustor of a gas turbine, comprising: calculating a first index indicating at least one of a recurrence between a plurality of multidimensional vectors having a plurality of data included in time-series data acquired from a first sensor installed in the combustor as vector components, or an intermittency between the plurality of multidimensional vectors; calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; detecting the sign based on the first indicator and the second indicator; Equipped with.

[0068] According to the above aspect (7), a sign is detected based on both a first index calculated based on time series data acquired from a first sensor and a second index calculated based on a plurality of time series data acquired from a second sensor and a third sensor. In the former, a first index indicating at least one of recurrence and intermittency of each data included in the time series data acquired by a single sensor is used as an index for detecting a sign. In the latter, a second index indicating synchronicity between a plurality of time series data acquired by a plurality of sensors arranged at different positions is used as an index for detecting a sign. In this way, by performing sign detection based on the first index and the second index that can evaluate combustion oscillation from different viewpoints, combustion oscillation having various oscillation characteristics can be detected early at the sign stage.

[0069] (8) A program for detecting an indication according to one embodiment of the present invention includes: 1. A program for detecting a sign of vibration occurring in a combustor of a gas turbine, comprising: A computer device includes: calculating a first index indicating at least one of a recurrence between a plurality of multidimensional vectors having a plurality of data included in time-series data acquired from a first sensor installed in the combustor as vector components, or an intermittency between the plurality of multidimensional vectors; calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; detecting the sign based on the first indicator and the second indicator; It is possible to execute the following.

[0070] According to the above aspect (8), a sign is detected based on both a first index calculated based on time series data acquired from a first sensor and a second index calculated based on a plurality of time series data acquired from a second sensor and a third sensor. In the former, a first index indicating at least one of recurrence and intermittency of each data included in the time series data acquired by a single sensor is used as an index for detecting a sign. In the latter, a second index indicating synchronicity between a plurality of time series data acquired by a plurality of sensors arranged at different positions is used as an index for detecting a sign. In this way, by performing sign detection based on the first index and the second index that can evaluate combustion oscillation from different viewpoints, combustion oscillation having various oscillation characteristics can be detected early at the sign stage. [Explanation of symbols]

[0071] 1. Gas turbine 2. Compressor 4. Combustor 6 Stator blade 8 Moving blades 10 Fuel Nozzle 12 Tailpiece 20a First sensor 20b Second sensor 20c 3rd sensor 100 Predictive Detection Device 110 Time series data acquisition section 120 Indicator calculation section 130 Predictive Detection Unit 140 Output section

Claims

1. A symptom detection device for detecting a symptom of vibration occurring in a combustor of a gas turbine, comprising: a first index calculation unit that calculates a first index indicating at least one of a regression between a plurality of multidimensional vectors, the plurality of data being included in time-series data acquired from a first sensor installed in the combustor, and an intermittency between the plurality of multidimensional vectors; a second index calculation unit for calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; a sign detection unit for detecting the sign based on the first indicator and the second indicator; An indication detection device comprising:

2. 2. The sign detection device according to claim 1, wherein the sign detection unit identifies a classification corresponding to the sign by applying a classification model to past data of the first indicator or the second indicator, and detects the sign based on whether the first indicator or the second indicator belongs to the classification.

3. 3. The symptom detection device according to claim 1, wherein the first index calculation unit calculates the first index indicating the recurrence as a parameter indicating a ratio of a recurrence point constituting a diagonal line structure included in a recurrence plot corresponding to the plurality of multidimensional vectors to all recurrence points.

4. 3. The symptom detection device according to claim 1, wherein the first index calculation unit calculates the first index indicating the intermittency as a parameter indicating a ratio of a regression point constituting a vertical structure or a horizontal structure included in a recurrence plot corresponding to the plurality of multidimensional vectors to all regression points.

5. 3. The sign detection device according to claim 1 or 2, wherein the second index calculation unit calculates the second index as at least one of an order parameter calculated from the plurality of time series data, a parameter indicating a ratio of regression points forming a diagonal structure included in a joint recurrence plot calculated from the plurality of time series data to all regression points, or a parameter indicating a ratio of regression points forming a diagonal structure included in a cross recurrence plot calculated from the plurality of time series data to all regression points.

6. The symptom detection device according to claim 1 , wherein the second sensor and the third sensor are arranged in a circumferential direction with respect to a central axis of the combustor.

7. A method for detecting a sign of vibration occurring in a combustor of a gas turbine, comprising: calculating a first index indicating at least one of a regression between a plurality of multidimensional vectors having a plurality of data included in time-series data acquired from a first sensor installed in the combustor as vector components, or an intermittency between the plurality of multidimensional vectors; calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; detecting the sign based on the first indicator and the second indicator; The symptom detection method includes:

8. 1. A program for detecting a sign of vibration occurring in a combustor of a gas turbine, comprising: A computer device includes: calculating a second index indicating at least one of a recurrence between a plurality of multidimensional vectors having a plurality of data included in time-series data acquired from a first sensor installed in the combustor as vector components, or an intermittency between the plurality of multidimensional vectors; calculating a second index indicating synchronism between a plurality of time-series data acquired from a second sensor and a third sensor, the second sensor and the third sensor being installed at different positions of the combustor; detecting the sign based on the first indicator and the second indicator; A predictive detection program that can execute the following:

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