Sign detection device and sign detection method

Abstract

To provide a sign detection device capable of detecting sudden vibration sufficiently prior to the occurrence of sudden vibration.SOLUTION: The sign detection device includes: multiple sensors; a data acquisition unit that acquires time-series fluctuation data of physical quantity from multiple sensors; a calculation unit that calculates a parameter representing the correlation between physical quantities at any two of the multiple positions from the time-series fluctuation data; and a detection unit that detects a sign of sudden vibration of a detection object based on the parameter representing the correlation. The multiple sensors are placed at multiple positions in the detection object respectively and each configured to measure physical quantity at each position.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a sign detection device and a sign detection method for detecting a sign of sudden vibration. [Background technology]

[0002] In machines such as gas turbines, steam turbines, engines, boilers, aircraft, and compressors, combustion vibration and shaft vibration can occur in the combustor, compressor, blades, etc. Among these vibrations, unstable vibrations that tend to change suddenly (sudden change vibrations) reach a limit cycle in a short time after an increase in vibration occurs. Reaching the limit cycle can lead to tripping or place a large burden on the equipment.

[0003] Therefore, it is desirable to avoid such sudden vibrations at an early stage. However, since the increase in vibration before the limit cycle is reached is short, there are cases where the sudden vibration cannot be avoided by control after the increase in vibration is detected. In order to avoid sudden vibrations, it is necessary to detect the signs of sudden vibrations well before their occurrence.

[0004] In recent years, detection techniques have been proposed that aim to detect sudden oscillations in advance. For example, Patent Document 1 discloses a device that detects combustion oscillations using a value related to the pressure in a combustor of a gas turbine. This device is configured to acquire a value related to the pressure in the combustor of the gas turbine, calculate network entropy, and detect the occurrence of combustion oscillations when the network entropy falls below a threshold value. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2018-80621 A Summary of the Invention [Problem to be solved by the invention]

[0006] As a result of intensive research by the inventors of the present application, it was found that the correlation between physical quantities (e.g., pressure) at multiple positions is important in detecting the signs of sudden vibration. By using parameters that indicate such correlation, it becomes possible to detect the signs of sudden vibration.

[0007] However, even if time-series fluctuation data of a physical quantity (a value related to the pressure in the combustor) at one position is acquired and network entropy is calculated as in Patent Document 1, the correlation between the physical quantities at multiple positions is not taken into consideration, so it is difficult to detect the signs of sudden vibration well before its occurrence.

[0008] In view of the above circumstances, an object of the present disclosure is to detect a sudden vibration sufficiently prior to the occurrence of the sudden vibration. [Means for solving the problem]

[0009] The sign detection device according to the present disclosure comprises: A plurality of sensors are arranged at a plurality of positions on the object to be detected, and configured to measure a physical quantity at each position; a data acquisition unit for acquiring time-series variation data of the physical quantity from the plurality of sensors; a calculation unit for calculating a parameter indicating a correlation between the physical quantities at any two positions among the plurality of positions from the time-series variation data; A detection unit for detecting a sign of a sudden vibration of the detection object based on the parameters; Equipped with.

[0010] The sign detection method according to the present disclosure includes: A step in which sensors arranged at a plurality of positions on a detection target measure a physical quantity at each position; acquiring time-series variation data of the physical quantity from a plurality of the sensors; calculating a parameter indicating a correlation between the physical quantities at any two positions among the plurality of positions from the time-series variation data; detecting a sign of a sudden vibration of the detection object based on the parameters; Includes. Effect of the Invention

[0011] According to the present disclosure, it is possible to detect a sudden vibration sufficiently before the sudden vibration occurs. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing a configuration of a sign detection device according to an embodiment; [Diagram 2] 1 is a schematic diagram showing an example of the arrangement of sensors in a sign detection device according to an embodiment; [Diagram 3] 2 is a schematic cross-sectional view showing an example of the arrangement of sensors in the sign detection device according to one embodiment. FIG. [Figure 4] 1 is a diagram illustrating an example of image data output by a sign detection device according to an embodiment. FIG. [Diagram 5] 1 is a diagram illustrating an example of image data output by a sign detection device according to an embodiment. FIG. [Figure 6A] 1 is a diagram illustrating an example of image data output by a sign detection device according to an embodiment. FIG. [Figure 6B] 1 is a diagram illustrating an example of image data output by a sign detection device according to an embodiment. FIG. [Figure 6C] 1 is a diagram illustrating an example of image data output by a sign detection device according to an embodiment. FIG. [Figure 7] 1 is a diagram illustrating an example of image data output by a sign detection device according to an embodiment. FIG. [Figure 8] 1 is a flowchart showing the steps of a sign detection method according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, some embodiments will be described with reference to the accompanying drawings. However, the dimensions, materials, shapes, relative arrangements, etc. of components described as the embodiments or shown in the drawings are merely illustrative examples and are not intended to limit the scope of the invention. For example, expressions expressing relative or absolute configuration, such as "in a certain direction," "along a certain direction," "parallel," "orthogonal," "center," "concentric," or "coaxial," not only strictly express such a configuration, but also express a state in which there is a relative displacement with a tolerance or an angle or distance to the extent that the same function is obtained. For example, expressions indicating that things are in an equal state, such as "identical," "equal," and "homogeneous," not only indicate a state of strict equality, but also indicate a state in which there is a tolerance or a difference to the extent that the same function is obtained. For example, expressions describing shapes such as a rectangular shape or a cylindrical shape do not only refer to rectangular shapes, cylindrical shapes, etc. in the strict geometric sense, but also refer to shapes that include uneven portions, chamfered portions, etc., to the extent that the same effect is obtained. On the other hand, the expressions "comprise," "include," "have," "includes," or "have" of one element are not exclusive expressions excluding the presence of other elements.

[0014] (Prediction detection device) The following describes the sign detection device 300 according to an embodiment. Fig. 1 is a block diagram showing the configuration of the sign detection device 300 according to an embodiment.

[0015] 1, the sign detection device 300 includes a plurality of sensors 200 and a calculation processing device 100 configured to execute calculation processing for detecting a sign of a sudden vibration. The sensor 200 is a sensor configured to measure a physical quantity of a detection target object.

[0016] The multiple sensors 200 are disposed at multiple positions on the detection object, respectively, and measure a physical quantity at each position. The physical quantity measured by the sensor 200 is, for example, one or more of pressure, strain, acceleration, velocity, and displacement. Note that the physical quantity measured by the sensor 200 is not limited to these physical quantities. The physical quantity measured by the sensor 200 may be any physical quantity that is highly related to the occurrence of combustion oscillation.

[0017] The arithmetic processing device 100 is a computer including, for example, a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), etc. In the arithmetic processing device 100, a processor (CPU) executes a program stored in a memory (RAM or ROM), thereby realizing various functions described later.

[0018] The following describes the functional configuration of the arithmetic processing device 100. As shown in FIG.

[0019] The data acquiring unit 110 is configured to acquire time-series fluctuation data of physical quantities from a plurality of sensors 200. The time-series fluctuation data is measurement data sampled at a plurality of timings (e.g., 100 or more timings) within a unit time (e.g., one second) in the recent past.

[0020] The calculation unit 120 is configured to calculate a parameter indicating a correlation from the time-series fluctuation data of the physical quantity acquired by the data acquisition unit 110. The parameter indicating a correlation is a parameter indicating a correlation between the physical quantities at any two positions among the multiple positions at which the sensor 200 is placed.

[0021] The parameter that indicates correlation is the correlation coefficient C ij or the correlation coefficient C ij The correlation coefficient C may be further processed (for example, link strength ρ). ijThe link strength ρ will be described later. The parameter indicating the correlation may be coherence, which indicates the similarity of the fluctuation characteristics when comparing time-series fluctuation data. The parameter indicating the correlation may be determinism of the cross-recurrence plot, order variable, maximal information coefficient, etc.

[0022] The detection unit 130 is configured to detect a sign of a sudden vibration of the object to be detected, based on the parameter indicating the correlation calculated by the calculation unit 120. A specific detection method will be described later.

[0023] The output unit 140 is configured to output image data indicating the vibration state of the detection target. For example, the output unit 140 outputs the image data to a display device (not shown) or a display unit (not shown) to display the image data. Specific examples of the image data will be described later. The output unit 140 may be configured to output audio data (for example, audio notifying of a sign of sudden vibration). In this case, the output unit 140 is configured to output the audio data to an audio output device such as a speaker.

[0024] The output unit 140 may be configured to output a predetermined signal when the detection unit 130 detects a sign of a sudden vibration. The predetermined signal is, for example, a stop signal for stopping the operation of the object to be detected, an output control signal for reducing the output of the object to be detected, a notification signal for notifying the user of a sign of a sudden vibration, or other signal effective for avoiding the sudden vibration.

[0025] The output unit 140 may be configured to output information related to maintenance estimated from the parameters indicating the correlation. The information related to maintenance is, for example, information on parts to be replaced, recommended replacement timing, the presence or absence of a malfunction, etc. Such image data, predetermined signals, and information related to maintenance are generated based on, for example, the calculation results of the calculation unit 120 and the detection results of the detection unit 130.

[0026] (Example of detection target and sensor placement) An example of the arrangement of the detection object and the sensor 200 according to one embodiment will be described below. Fig. 2 is a schematic diagram showing an example of the arrangement of the sensor 200 of the sign detection device 300 according to one embodiment. This figure shows a cross section along a direction perpendicular to the turbine shaft of the gas turbine 20. Fig. 3 is a schematic cross-sectional view showing an example of the arrangement of the sensor 200 of the sign detection device 300 according to one embodiment. This figure shows a cross section along the turbine shaft of the gas turbine 20.

[0027] In one embodiment, the detection target of the sign detection device 300 may be, for example, the gas turbine 20 shown in Fig. 2 and Fig. 3. Note that the detection target may not be the gas turbine 20, but may be, for example, a machine such as a steam turbine, an engine, a boiler, an aircraft, or a compressor.

[0028] As shown in Fig. 2 and Fig. 3, the gas turbine 20 includes a compressor 7, a combustor 8, a stator vane 4, and a rotor blade 6. As shown in Fig. 2, the combustor 8 includes eight can-type combustors. In Fig. 2, the eight can-type combustors are numbered #1 to #8 according to their arrangement positions. As shown in Fig. 3, each of the eight can-type combustors has a fuel nozzle 9, an inner cylinder 2, and a transition piece 3. The sensor 200 is a pressure sensor for measuring the pressure inside the combustor 8. The sensor 200 is disposed in each of the eight transition pieces 3.

[0029] In this example, the sensor 200 is disposed in the transition piece 3 of the combustor 8 of the gas turbine 20. However, the location of the sensor 200 is not limited to this example. The sensor 200 only needs to be disposed in a position where the vibration mode can be observed, and may be disposed in a compressor, a blade, a bearing, or the like depending on the type of object to be detected.

[0030] (Specific examples of parameters that show correlation) Hereinafter, specific examples of parameters (correlation coefficient and link strength) indicating the correlation obtained by the calculation unit 120 through calculation will be described.

[0031] First, the correlation between physical quantities corresponding to the positions of the multiple sensors 200 acquired by the data acquisition unit 110 will be described. According to the inventors of the present application, the correlation between the physical quantities can be interpreted as an undirected weighted complex network with each of the multiple positions as a node. For example, the correlation between the physical quantities can be expressed as an adjacency matrix A (A is expressed in bold to indicate a vector; the same applies below).

[0032] As shown in the following formula (1), the adjacency matrix A is defined as an n × n square matrix. In the adjacency matrix A, any matrix element w ij indicates the correlation between the i-th physical quantity and the j-th physical quantity, and n corresponds to the number of physical quantities (i.e., the number of sensors 200). TIFF0007599281000001.tif17170

[0033] In the adjacency matrix A, the diagonal elements w 11、 w 22、··· w nn The value of is zero, and the matrix elements other than the diagonal elements are values ​​that indicate the magnitude of the correlation coefficient (the matrix element in the i-th row and j-th column is the correlation coefficient C ij In other words, the matrix elements that show the relationship between physical quantities at different positions are the correlation coefficients C ij The absolute value of w is the matrix element that indicates the relationship between physical quantities at the same position. Also, even if the row and column numbers are swapped, the correlation coefficient will basically be the same. For example, w 24 And w 42 will have the same value.

[0034] In the example of the arrangement of the sensor 200 shown in FIG. 2, there are eight nodes #1 to #8, so that the adjacency matrix A is an 8×8 square matrix. For example, the correlation between the physical quantities measured in the can-type combustor #2 and the can-type combustor #4 is expressed by the matrix element w 24 That is, the correlation coefficient C 24 is the absolute value at

[0035] The parameter that indicates the correlation is the correlation coefficient C, which indicates the correlation of the fluctuation of the physical quantity at each position. ij The correlation coefficient C ijis expressed, for example, by the following formula (2): Note that, although formula (2) shows an example in which the physical quantity is pressure, the physical quantity may be a physical quantity other than pressure. TIFF0007599281000002.tif22170

[0036] Here, N is the number of samples (e.g., 100 or more) per unit time (e.g., 1 second). i (t) denotes the instantaneous value of pressure at the i-th position, and p j (t) indicates the instantaneous value of pressure at the jth position. P i p i (t) is the time average value in unit time, P j p j The time average value in unit time (t) is used. Note that, instead of the instantaneous value or time average value of the pressure, the instantaneous value or time average value of the pressure fluctuation amount may be used.

[0037] Correlation coefficient C ij When the patterns of change in the physical quantity at two locations are similar, the value is large, and when they are not similar, the value is small. The correlation coefficient C ij The value of is normalized to be in the range of 0 to 1. Therefore, the correlation coefficient C ij The strength of correlation can be determined from the correlation coefficient C ij is not limited to the calculated value shown in formula (2) and can be changed appropriately within a range that does not impair the essential meaning.

[0038] The parameter indicating the correlation may be link strength ρ. Link strength ρ is an index indicating the degree of connection between vertices of a network. Link strength ρ is expressed, for example, by the following formula (3), where n is the number of nodes. TIFF0007599281000003.tif13170

[0039] The numerator of the link strength ρ is the correlation coefficient C for all combinations of two positions selected from multiple positions. ij The correlation coefficient C is calculated by calculating the sum of the correlation coefficients.ij The sum of the elements w of the adjacency matrix A ij is the same as the sum of

[0040] The denominator of the link strength ρ corresponds to the number of combinations. For example, in the example arrangement of the sensor 200 shown in FIG. 2, since there are eight nodes #1 to #8, the denominator of the link strength ρ is 56. In this case, the link strength ρ is the sum of the 56 matrix elements divided by 56, and therefore is a normalized value of the sum of the correlation coefficients. In this case, the link strength ρ is calculated as a value within the range of 0 to 1, which is advantageous in that the link strength ρ can be quantitatively grasped.

[0041] (Examples of image data) A specific example of image data output by the output unit 140 will be described below. For example, the calculation unit 120 calculates the correlation coefficient C ij In this case, the output unit 140 may calculate the correlation coefficient C ij The image data associated with the above may be output.

[0042] FIG. 4 is a diagram showing an example of image data output by the sign detection device 300 according to an embodiment. This diagram shows an example of an image displayed during stable combustion. FIG. 5 is a diagram showing an example of image data output by the sign detection device 300 according to an embodiment. This diagram shows an example of an image displayed during combustion oscillation (combustion oscillation includes sudden oscillation). FIGS. 6A, 6B, and 6C are each a diagram showing an example of image data output by the sign detection device 300 according to an embodiment. These diagrams show image data at different times.

[0043] For example, as shown in FIG. 4, FIG. 5, FIG. 6A, FIG. 6B, and FIG. 6C, image data is arranged such that a plurality of positions are arranged as nodes, and the nodes are correlated with each other by a correlation coefficient C. ij The image data may be image data showing a figure connected by lines in a different display format depending on the size of the image.

[0044] For example, in the diagrams shown in Figs. 4, 5, 6A, 6B, and 6C, the correlation coefficient C ij When the magnitude of is small, black lines are drawn between the nodes, and the correlation coefficient C ij When the magnitude of is large, gray lines connect the nodes. Therefore, during stable combustion, all the lines are black, as shown in Figure 4. On the other hand, during combustion oscillation, all the lines are gray, as shown in Figure 5. When it is unclear whether the combustion is stable or oscillating, some lines are gray and others are black, as shown in Figures 6A, 6B, and 6C.

[0045] The different display formats are, for example, one or more of the following: color, shading, thickness, line type (solid line, dotted line, dashed line), display or non-display, etc. ij For example, the line connecting the nodes may be a display format that allows the magnitude of the correlation coefficient C ij is displayed as a red line when it is large, and the correlation coefficient C ij If the correlation coefficient C is small, it may be displayed as a blue line. For example, the line connecting the nodes is ij is displayed as a solid line when it is large, and the correlation coefficient C ij If is small, it may be displayed as a dotted line.

[0046] In the example shown, two lines, black and gray, represent the correlation coefficient C ij However, in some embodiments, the magnitude of the correlation coefficient C ij The display format of the line may be changed in stages according to the magnitude of the correlation coefficient C ij The display format of the line may change depending on whether the magnitude of exceeds the reference value, and the correlation coefficient C ij The display format of the line may be changed in multiple stages depending on the size of the line.

[0047] The nodes indicated by the image data are preferably arranged in a positional relationship corresponding to a plurality of positions of the actual physical quantities. For example, as shown in Fig. 4, Fig. 5, Fig. 6A, Fig. 6B, and Fig. 6C, the image data may be image data in which the nodes are arranged simulating the arrangement of the sensors 200 in each of the can-type combustors (#1 to #8) of the combustor 8 included in the gas turbine 20.

[0048] The image data is not limited to the example shown in the figure. For example, the image data may be obtained by using a correlation coefficient C ij The size of may be indicated by a numerical value, a symbol, etc. The image data may be image data shown in a display format such as a round-robin table (not shown).

[0049] By the way, referring to FIG. 6A, FIG. 6B, and FIG. 6C, the color of the lines connecting the nodes mainly indicates the correlation coefficient C 23 (C 32 ), C 24 (C 42 ), C 34 (C 43 ) is large. In this case, if maintenance is performed on the can-type combustors #2, #3, and #4, it may be possible to suppress the combustion oscillation. In this way, it is also possible to identify parts that may cause the combustion oscillation from the image data output by the output unit 140.

[0050] For example, the calculation unit 120 calculates the correlation coefficient C for all combinations of two positions selected from a plurality of positions (positions at which the sensor 200 is disposed). ij Calculate the correlation coefficient C ij In this case, the output unit 140 may output image data indicating the time transition of the link strength p calculated by the calculation unit 120.

[0051] 7 is a diagram showing an example of image data output by the sign detection device 300 according to one embodiment. This diagram is a graph showing the transition over time of the link strength ρ calculated by the calculation unit 120. The horizontal axis represents time, and the vertical axis represents the magnitude of the link strength ρ.

[0052] The link strength ρ is the time when the sudden vibration occurs (for example, at time T d From time T e The link strength ρ is close to 1 during the time from the time of the sudden vibration (for example, at time T a , T b , T c ) may show a value larger than normal (for example, a value of 0.3 or more). In such a case, the detection unit 130 or an operator looking at the image data may determine that a sign of sudden vibration has occurred.

[0053] (Example of detecting signs of sudden vibration) The detection unit 130 detects a sign of a sudden vibration based on the calculation results of the above-mentioned calculation unit 120. A specific detection method will be described below.

[0054] The detection unit 130 detects the correlation coefficient C ij In this case, for example, the detection unit 130 may detect a sign of a sudden vibration based on the magnitude of the correlation coefficient C ij When the magnitude of the correlation coefficient C exceeds the standard value, ij If the number of combinations of these exceeds a predetermined number (for example, n / 2), it may be determined that this is a sign of sudden vibration. If the duration of such a state exceeds a reference value, the detection unit 130 may determine that this is a sign of sudden vibration. In this way, the individual correlation coefficients C ij The size of the correlation coefficient C that is greater than the standard value ij A sign of sudden vibration may be detected based on the number of combinations of the above or the duration of the state.

[0055] The detection unit 130 may detect a sign of a sudden vibration based on the link strength ρ. In this case, for example, the detection unit 130 may determine whether or not there is a sign of a sudden vibration depending on whether or not the value of the link strength ρ exceeds a reference value. For example, in the examples shown in FIGS. 6A, 6B, and 6C, the correlation coefficient C ij Even if you focus on the size of the change, it may be difficult to distinguish subtle changes.

[0056] In contrast, in a detection method that focuses on the link strength ρ, a unique discrimination criterion can be set. For example, as shown in FIG. 7, when the detection unit 130 determines that a link strength ρ is a sign of a sudden vibration when the magnitude of the link strength ρ is equal to or greater than a reference value (e.g., 0.3), discrimination is easy. In addition, with such a configuration, the correlation coefficient C ij Since the link strength ρ based on the above is used for predictive detection, predictive detection that evaluates the overall state of the detection object is possible, rather than predictive detection that focuses only on between specific positions of the detection object.

[0057] The detection unit 130 detects a parameter (correlation coefficient C ij The data acquisition unit 110 may be configured to detect a sign of a sudden vibration based on the link strength ρ and amplitude information of a physical quantity indicated by the time-series fluctuation data acquired by the data acquisition unit 110. The amplitude information of a physical quantity is information on the amplitude of a physical quantity, and is, for example, information indicating the maximum value of a fluctuation component of a physical quantity, the root mean square of a fluctuation component of a physical quantity, etc. The amplitude of a physical quantity means the deviation from a time average value, that is, the magnitude of the fluctuation. Note that the amplitude of a physical quantity is rarely a constant value like the amplitude of a waveform with regular amplitude and period such as a sine wave or a square wave.

[0058] Here, a case where the detection object is the gas turbine 20 will be described as an example. The detection unit 130 or the calculation unit 120 may correct the link strength ρ by using, for example, amplitude information of a physical quantity (pressure or temperature) indicated by the time-series data so that the link strength ρ is increased when the combustion temperature is low and the link strength ρ is decreased when the combustion temperature is high. The correction may be a calculation of multiplication by a weighting coefficient. This improves the reliability of the link strength ρ as an indicator of a sign of sudden vibration.

[0059] Also, instead of correcting the link strength, amplitude information of a physical quantity (pressure or temperature) indicated by the time-series data may be used as material for verifying the validity of the link strength p. For example, the detection unit 130 may be configured to determine that it is a sign of sudden vibration only when the amplitude information is equal to or greater than a reference value and the link strength p is higher than the reference value.

[0060] Correlation coefficient C ij The amplitude information of the physical quantity may be used to correct or verify the validity of the link strength ρ. That is, the detection method of the detection unit 130 is not limited to the detection method that combines the amplitude information of the physical quantity and the link strength ρ as described above. The output unit 140 may be configured to output image data after adjusting a parameter indicating a correlation by using the amplitude information of the physical quantity.

[0061] For example, the element w of the adjacency matrix A shown in the above formula (1) ij In the matrix, the off-diagonal elements are the correlation coefficients C ij For example, the matrix elements other than the diagonal elements of the adjacency matrix A may be expressed as the correlation coefficient C ij is 0.5 or more and the average value of the maximum amplitude of the physical quantity P ij If the condition of 0.4 or more is satisfied, the value is 1, and if the condition is not satisfied, the value may be 0. The average value P ij p i (t) maximum value and p j (t) and the maximum value of (t) divided by 2.

[0062] When such an adjacency matrix A is used, the matrix elements become 1 or 0. Therefore, in the image data output by the output unit 140, the lines connecting the nodes may be displayed in a manner that distinguishes between 1 and 0 by the presence or absence of a line (for example, a display format in which a line is displayed for 1 and no line is displayed for 0) or by two types of lines (for example, a display format in which a blue line is displayed for 1 and a red line is displayed for 0). By using such amplitude information of physical quantities, it is possible to improve the accuracy of the detection of a sign of sudden vibration.

[0063] (Prediction detection method) A specific example of the sign detection method will be described below with reference to FIG. 8. FIG. 8 is a flowchart showing the steps of the sign detection method according to one embodiment. Note that some or all of the steps described below may be executed manually by a user. In the sign detection method described below, each step can be modified as appropriate to correspond to the processing executed by the sign detection device 300 described above. In the following description, descriptions that overlap with the description of the sign detection device 300 will be omitted.

[0064] 8, first, a plurality of sensors 200 arranged at a plurality of positions on a detection target measure a physical quantity at each position (step S1). Time-series fluctuation data of the physical quantity measured by each of the sensors 200 is acquired from the plurality of sensors 200 (step S2).

[0065] Next, a parameter indicating the correlation between physical quantities at any two of the multiple positions is calculated from the time-series variation data (step S3). Specifically, the correlation coefficient C ij The parameters showing the correlation, such as the size of the detection object, the link strength across the entire detection object, etc. are calculated. In addition to the parameters showing the correlation, the correlation coefficient C ij A calculation such as correction based on the number of combinations, the duration of the state, and the amplitude of the physical quantity may be performed. Based on the parameters indicating the correlation calculated in step S3, a sign of sudden vibration of the detection object is detected (step S4).

[0066] These steps S1 to S4 may be periodically repeated. This makes it possible to monitor for signs of sudden vibration. When a sign of sudden vibration is detected, the above-mentioned predetermined signal (such as a stop signal or an alarm signal) may be output. Also, the above-mentioned image data may be output and the image may be displayed on a display device or the like.

[0067] The present disclosure is not limited to the above-described embodiments, and includes modifications to the above-described embodiments and appropriate combinations of multiple embodiments.

[0068] For example, when the object to be detected is a compressor, multiple sensors 200 for measuring pressure may be arranged at multiple positions on the compressor. When the object to be detected is an axial compressor, multiple sensors 200 may be arranged in the circumferential direction of its outlet. When the object to be detected is a centrifugal compressor, multiple sensors 200 may be arranged in the annular direction. When detecting a sign of sudden vibration in blade vibration, multiple sensors 200 may be arranged at the base of the blade.

[0069] When detecting a sign of sudden axial vibration, the sensor 200 may be disposed at each of the different bearing positions. In this case, since the sensor arrangement is one-dimensional, the image data output by the output unit 140 may show a figure in which a plurality of nodes are arranged in a straight line and connected between them by lines.

[0070] When the detection target is a steam turbine, a strain gauge may be used as the sensor 200. For example, a plurality of sensors 200 may be disposed at the roots of the blades of the steam turbine arranged along the circumferential direction in the same stage.

[0071] When the object to be detected is a rocket engine, there may be only one combustor. However, even in this case, multiple sensors 200 may be arranged in the circumferential direction of the outlet of the combustor, and the precursor detection device 300 may be configured to detect a precursor of sudden vibration. When the object to be detected is an aircraft, the method of detecting a precursor of sudden vibration by the precursor detection device 300 may be applied to the engine or the wing. In this way, by arranging multiple sensors 200 in the circumferential direction of the cross section at the position where combustion vibration occurs, it is possible to detect a precursor of sudden vibration of various objects to be detected.

[0072] (summary) The contents described in each of the above embodiments can be understood, for example, as follows.

[0073] (1) The symptom detection device (300) according to the present disclosure is A plurality of sensors (200) are arranged at a plurality of positions on a detection target (e.g., a gas turbine 20) and configured to measure a physical quantity at each position; a data acquisition unit (110) for acquiring time-series fluctuation data of the physical quantity from the plurality of sensors (200); a calculation unit (120) for calculating a parameter indicating a correlation between the physical quantities at any two positions among the plurality of positions from the time-series variation data; A detection unit (130) for detecting a sign of a sudden vibration of the detection object based on the parameters; Equipped with.

[0074] According to the configuration described in (1) above, a sign of a sudden vibration of a detection object is detected based on a parameter indicating a correlation between physical quantities between two positions. Therefore, the sign can be detected sufficiently before the sudden vibration occurs.

[0075] (2) In some embodiments, in the configuration described in (1) above, The parameter is a correlation coefficient that indicates the correlation of the fluctuation of the physical quantity at each position.

[0076] According to the configuration method described in (2) above, since the correlation coefficient indicating the correlation between the fluctuations of the physical quantities at each position is used as a parameter, it is possible to clarify the judgment criteria for the sign detection. In addition, it is possible to estimate a position that is highly related to a sudden vibration or a sign thereof based on the magnitude of the parameter indicating the correlation. Such an estimation result is useful information for the maintenance of the detection target (e.g., the gas turbine 20).

[0077] (3) In some embodiments, in the configuration described in (2) above, the calculation unit (120) calculates the correlation coefficient for all combinations of two positions selected from the plurality of positions, and calculates a link strength from the sum of the calculated correlation coefficients; The detection unit (130) detects a sign of the sudden vibration based on the link strength.

[0078] According to the configuration described above in (3), the link strength based on the correlation coefficient of all combinations of multiple positions is used for predictive detection, which makes it possible to perform predictive detection that evaluates the overall condition of the detected object, rather than predictive detection that focuses only on between specific positions of the detected object (e.g., gas turbine 20).

[0079] (4) In some embodiments, in the configuration described in (2) or (3) above, The calculation unit (120) calculates the correlation coefficient for all combinations of two positions selected from the plurality of positions, The image processing device further includes an output unit (140) configured to output image data that associates each of the plurality of positions with the correlation coefficient calculated for each combination of positions.

[0080] According to the configuration described in (4) above, the operator can visually grasp the state of the correlation coefficient between a plurality of positions based on the image data. As a result, it becomes possible to easily know information necessary for operation monitoring or maintenance, such as the presence or absence of a sign of sudden vibration, and positions related to the sign of sudden vibration.

[0081] (5) In some embodiments, in the configuration described in (4) above, The image data is image data showing a figure in which the plurality of positions are arranged as a plurality of nodes, and the nodes are connected by lines in different display formats according to the magnitude of the correlation coefficient.

[0082] According to the configuration described in (5) above, the operator can grasp at a glance the state of the correlation coefficient between a plurality of positions based on the image data.

[0083] (6) In some embodiments, in the configuration described in (4) above, the calculation unit (120) calculates the correlation coefficient for all combinations of two positions selected from the plurality of positions, and calculates a link strength from the sum of the calculated correlation coefficients; The image data is image data that indicates a change in the link strength over time.

[0084] According to the configuration described in (6) above, an operator can monitor the trend of the link strength and check for signs of sudden vibration changes.

[0085] (7) In some embodiments, in the configuration described in any one of (1) to (6) above, The detection unit (130) detects a sign of the sudden vibration based on the parameter and amplitude information of the physical quantity indicated by the time-series fluctuation data.

[0086] According to the configuration described in (7) above, the detection unit (130) detects a sign of a sudden vibration by combining a parameter indicating a correlation with amplitude information of a physical quantity indicated by the time-series fluctuation data. This improves the detection accuracy. According to the knowledge of the present inventors, for example, when the detection target is a gas turbine (20), the same parameter (e.g., link strength) tends to change easily when the combustion temperature is high, whereas it tends not to change easily when the combustion temperature is low. In such a case, the accuracy can be improved by detecting a sign of a sudden vibration by combining a parameter (e.g., link strength) with amplitude information of a physical quantity.

[0087] (8) The sign detection method according to the present disclosure includes: A step in which a sensor (200) arranged at each of a plurality of positions on a detection target (e.g., a gas turbine 20) measures a physical quantity at each position; acquiring time-series variation data of the physical quantity from a plurality of the sensors (200); calculating a parameter indicating a correlation between the physical quantities at any two positions among the plurality of positions from the time-series variation data; detecting a sign of a sudden vibration of the detection object based on the parameters; Includes.

[0088] According to the configuration described in (8) above, a sign of a sudden vibration of a detection object (for example, the gas turbine 20) is detected based on a parameter indicating the correlation of physical quantities between two positions. Therefore, the sign of a sudden vibration can be detected well before the occurrence of the sudden vibration. [Explanation of symbols]

[0089] 2 Inner cylinder 3 Tailpiece 4 Stator blade 6 Moving blades 7 Compressor 8. Combustor 9 Fuel Nozzle 20 Gas Turbine 100 Processing unit 110 Data Acquisition Section 120 Arithmetic section 130 Detection unit 140 Output section 200 Sensors 300 Predictive Detection Device

Claims

1. A plurality of sensors are arranged at a plurality of positions on the object to be detected, and configured to measure a physical quantity at each position; a data acquisition unit for acquiring time-series variation data of the physical quantity from the plurality of sensors; a calculation unit for calculating a parameter indicating a correlation between the physical quantities at any two positions among the plurality of positions from the time-series variation data; A detection unit for detecting a sign of a sudden vibration of the detection object based on the parameters; Equipped with the detection object is a gas turbine including a plurality of combustors, the plurality of sensors are configured to measure pressures inside the plurality of combustors as the physical quantities, respectively; The parameter is a correlation coefficient that indicates the correlation of the fluctuation of the physical quantity at each position at the same time. Early warning detection device.

2. the calculation unit calculates the correlation coefficient for all combinations of two positions selected from the plurality of positions, and calculates a link strength from a sum of absolute values ​​of the calculated correlation coefficients; The detection unit detects a sign of the sudden vibration based on the link strength. The symptom detection device according to claim 1 .

3. the calculation unit calculates the correlation coefficient for all combinations of two positions selected from the plurality of positions; an output unit configured to output image data in which each of the plurality of positions is associated with the correlation coefficient calculated for each combination of the positions; The symptom detection device according to claim 1 or 2.

4. The image data is image data showing a figure in which the plurality of positions are arranged as a plurality of nodes, and the nodes are connected by lines in different display formats according to the magnitude of the correlation coefficient. The symptom detection device according to claim 3 .

5. the calculation unit calculates the correlation coefficient for all combinations of two positions selected from the plurality of positions, and calculates a link strength from a sum of absolute values ​​of the calculated correlation coefficients; The image data is image data showing a temporal change in the link strength. The symptom detection device according to claim 3 .

6. The detection unit detects a sign of the sudden vibration based on the parameter and amplitude information of the physical quantity indicated by the time-series fluctuation data. The symptom detection device according to any one of claims 1 to 5.

7. A step in which a plurality of sensors arranged at a plurality of positions on a detection target measure a physical quantity at each position; acquiring time-series variation data of the physical quantity from the plurality of sensors; calculating a parameter indicating a correlation between the physical quantities at any two positions among the plurality of positions from the time-series variation data; detecting a sign of a sudden vibration of the detection object based on the parameters; Including, the detection object is a gas turbine including a plurality of combustors, In the step of measuring the physical quantity, the plurality of sensors measure pressures inside the plurality of combustors as the physical quantity, respectively; The parameter is a correlation coefficient that indicates the correlation of the fluctuation of the physical quantity at each position at the same time. Predictive detection method.

8. In the calculating step, the correlation coefficient is calculated for all combinations of two positions selected from the plurality of positions, and a link strength is calculated from a sum of absolute values ​​of the calculated correlation coefficients; In the detecting step, a sign of the sudden vibration is detected based on the link strength. The symptom detection method according to claim 7 .

9. In the calculating step, the correlation coefficient is calculated for all combinations of two positions selected from the plurality of positions; The method further includes an outputting step configured to output image data associating each of the plurality of positions with the correlation coefficient calculated for each combination of positions, In the calculating step, the correlation coefficient is calculated for all combinations of two positions selected from the plurality of positions, and a link strength is calculated from a sum of absolute values ​​of the calculated correlation coefficients; The image data is image data showing a temporal change in the link strength. The symptom detection method according to claim 7 .

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