Deformation estimation device, deformation estimation system, and deformation estimation method

Abstract

This deformation estimation device (60) is provided with a control unit (50) for estimating deformation occurring in a structure (1) on the basis of a measurement value indicating deformation of a measurement surface on the surface of a structure (1). The control unit (50): predicts predicted deformation information indicating, by a probability distribution, deformation occurring on the measurement surface of the structure (1) in a plurality of boundary conditions applied to the structure (1); sets a first observation equation indicating the correlation between a first observation variable indicating the measurement value and the error of the measurement value by a probability distribution and the predicted deformation information serving as a first state variable; and estimates deformation occurring in the structure on the basis of the first observation equation.

Description

[Technical Field]

[0001] The present application relates to a deformation estimation device, a deformation estimation system, and a deformation estimation method. [Background technology]

[0002] For example, accurately estimating and understanding the deformation that occurs in various structures such as electric power equipment, steel towers, and steel bridges is important for assessing the life span of these structures and for performing maintenance and conservation work. However, prediction errors, measurement errors, etc. occur in the process of predicting and measuring the deformation of a structure. In order to reduce such errors and accurately estimate the deformation that occurs in a structure, a material testing machine as a deformation estimation device with the following configuration has been disclosed.

[0003] That is, in a conventional measurement method for measuring displacement and strain fields using a materials testing machine as a deformation estimation device, fixed noise in a grayscale image of a test piece captured by a video camera is probabilistically modeled based on darkness, and this probabilistic model of fixed noise is used to evaluate the uncertainty of the measurement of the center of gravity of a dot affixed to the surface of the test piece when a load is applied. Then, from the measurement results of the dot center of gravity, the dot displacement, displacement field, and strain field are calculated probabilistically using the law of uncertainty propagation. By performing measurements probabilistically using the dot center of gravity tracking method, uncertainty is propagated to each physical quantity derived by calculation from the observed value, and the reliability of the measurement value is presented to the user (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. WO2015 / 049757 (paragraphs

[0028] to

[0032] ) Summary of the Invention [Problem to be solved by the invention]

[0005] However, such conventional measurement methods for measuring displacement and strain fields using material testing machines as deformation estimation devices were unable to sufficiently reduce errors that occurred during the measurement process of a structure, and there were cases where the deformation occurring in the structure could not be accurately estimated. The present application discloses technology for solving the above-mentioned problems, and aims to provide a deformation estimation device, a deformation estimation system, and a deformation estimation method that can estimate deformation occurring in a structure with high accuracy. [Means for solving the problem]

[0006] The deformation estimation device disclosed in the present application comprises: 1. A deformation estimation device including a control unit that estimates deformation occurring in a structure based on measurement values ​​indicating deformation on a measurement plane of a surface of the structure, The control unit predicting predicted deformation information that indicates, by a probability distribution, deformations that occur on the measurement surface of the structure under a plurality of boundary conditions that are applied to the structure; setting a first observation equation that indicates a correlation between a first observation variable that indicates the measurement value and an error in the measurement value by a probability distribution and the predicted deformation information as a first state variable, and estimating the deformation that will occur in the structure based on the first observation equation; It is something. The deformation estimation system disclosed in the present application also includes: a deformation estimation device configured as described above; an output unit that displays an output from the control unit, The control unit setting at least the longitudinal elastic modulus and the Poisson's ratio of the structure as material characteristic values ​​of the structure in the prediction of the predicted deformation information; a soundness determination that determines whether the estimated deformation occurring in the structure and the probability distribution of the error thereof exceed a first threshold value as an index value indicating soundness, and displays the determination result on the output unit; It is something. The deformation estimation method disclosed in the present application also includes: A deformation estimation method using the deformation estimation device configured as described above, The control unit a step of predicting predicted deformation information that indicates, by a probability distribution, deformations that occur on the measurement surface of the structure under a plurality of boundary conditions that are applied to the structure; setting a first observation equation that indicates a correlation between a first observation variable (u*) that indicates the measurement value and an error in the measurement value by a probability distribution and the predicted deformation information as a first state variable, and estimating the deformation that will occur in the structure based on the first observation equation; It is something. [Effects of the Invention]

[0007] According to the deformation estimation device and deformation estimation method disclosed in the present application, it is possible to obtain a deformation estimation device and deformation estimation method that can estimate deformation occurring in a structure with high accuracy. Furthermore, the deformation estimation system disclosed in the present application estimates deformation occurring in a structure with high accuracy and then determines whether the deformation occurring is within an allowable value, allowing managers to appropriately assess the lifespan of the structure, and perform maintenance and conservation work, etc. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a schematic configuration of a deformation estimation device according to a first embodiment and a deformation estimation system including the deformation estimation device. [Figure 2] 4 is a flowchart showing the flow of control processing by a variable output unit included in the deformation estimation device in accordance with the first embodiment. FIG. [Figure 3] 4 is a flowchart showing the flow of control processing of a model generation unit included in the deformation estimation device in accordance with the first embodiment. FIG. [Figure 4] 4 is a flowchart showing the flow of control processing of an estimating unit included in the deformation estimation device according to the first embodiment. FIG. [Figure 5] 1 is a cutaway perspective view showing a part of a structure to be inspected according to the first embodiment. FIG. [Figure 6]Fig. 6A is a diagram for explaining a process for setting a boundary condition at one cross section of a structure according to embodiment 1. Fig. 6B is a diagram for explaining a process for setting a boundary condition at the other cross section of a structure according to embodiment 1. [Figure 7] 4 is a flowchart showing the flow of control processing of an estimating unit included in the deformation estimation device according to the first embodiment. FIG. [Figure 8] 4 is a flowchart showing the flow of control processing of an estimating unit included in the deformation estimation device according to the first embodiment. FIG. [Figure 9] FIG. 10 is a block diagram showing a schematic configuration of a deformation estimation device according to a second embodiment and a deformation estimation system including this deformation estimation device. [Figure 10] FIG. 10 is a block diagram showing a schematic configuration of a measurement unit included in a deformation estimation device according to a second embodiment. [Figure 11] 10 is a flowchart showing the flow of control processing of a variable output unit included in a measurement unit according to the second embodiment. FIG. [Figure 12] 10 is a flowchart showing the flow of control processing of a model generation unit included in a measurement unit according to the second embodiment. FIG. [Figure 13] FIG. 10 is a flowchart showing the flow of control processing of a measurement unit according to the second embodiment. [Figure 14] FIG. 10 is a conceptual diagram for explaining the control process of the measurement unit for the structure that is the inspection target according to the second embodiment. [Figure 15] FIG. 10 is a block diagram showing a schematic configuration of a deformation estimation device according to a third embodiment and a deformation estimation system including this deformation estimation device. [Figure 16] FIG. 11 is a flowchart showing the flow of control processing of an estimating unit and a crack estimating unit provided in the deformation estimating device according to the third embodiment. [Figure 17] FIG. 10 is a block diagram showing a schematic configuration of a deformation estimation system according to a fourth embodiment. [Figure 18] FIG. 2 is a diagram illustrating an example of a hardware configuration of a control unit. [Figure 19] FIG. 2 is a diagram illustrating an example of a hardware configuration of a control unit. DETAILED DESCRIPTION OF THE INVENTION

[0009] Embodiment 1 Preferred embodiments of a deformation estimation device, a deformation estimation system, and a deformation estimation method according to the present invention will be described below with reference to the drawings. The same contents and corresponding parts are designated by the same reference numerals, and detailed descriptions thereof will be omitted. Similarly, in the following embodiments, redundant descriptions of components designated by the same reference numerals will be omitted.

[0010] FIG. 1 is a block diagram showing a schematic configuration of a deformation estimation device 60 according to the first embodiment and a deformation estimation system 100 including the deformation estimation device 60. As shown in FIG. FIG. 2 is a flowchart showing the flow of control processing of the variable output unit 20 included in the deformation estimation device 60 shown in FIG. FIG. 3 is a flowchart showing the flow of control processing of the model generating unit 30 included in the deformation estimation device 60 shown in FIG. FIG. 4 is a flowchart showing the flow of control processing of the estimating unit 40 included in the deformation estimating device 60 shown in FIG. FIG. 5 is a cutaway perspective view showing a portion of the structure 1 to be inspected. FIG. 6A is a diagram for explaining the process of setting boundary conditions at cross section 1A of the structure shown in FIG. FIG. 6B is a diagram for explaining the process of setting boundary conditions at cross section 1B of the structure shown in FIG.

[0011] A deformation estimation device 60 of this embodiment shown in FIG. 1 estimates deformation occurring in a structure 1 to be inspected. Furthermore, the deformation estimation system 100 of this embodiment includes a deformation estimation device 60 and an output unit 5 that outputs the estimation results of this deformation estimation device 60, and notifies the estimated deformation to a manager performing maintenance and conservation work via the output unit 5.

[0012] The output unit 5 that notifies the administrator may have a display function such as a display, or may have an audio output function such as a speaker. The configuration of the output unit 5 is not limited as long as it has a function that can notify the administrator of the estimation result in the deformation estimation device 60.

[0013] 5, of the surfaces 1S, 1A, 1B, 1C, and 1D of the structure 1 shown, the surface 1S is the surface that can be observed directly, and the surfaces that cannot be observed directly are cross sections 1A, 1B, 1C, and 1D obtained by cutting the structure 1 parallel to the X-axis and Y-axis directions. As will be described below, the deformation estimation device 60 measures a measurement surface 1M, which is a set area within the observable surface 1S.

[0014] The configuration of the deformation estimation device 60 will be described. As shown in FIG. 1, the deformation estimation device 60 includes a measurement unit 10 that measures the deformation on the measurement surface 1M of the surface 1S of the structure 1 as a measurement value, and a control unit 50 that estimates the deformation occurring in the structure 1 based on this measurement value. The detailed configuration of the measurement unit 10 is not limited, but for example, it may acquire the position of a point on the measurement surface 1M before deformation occurs, acquire the position of this point after deformation occurs, and determine the deformation from the displacement of the position. The control unit 50 includes a variable output unit 20, a model generation unit 30, and an estimation unit 40. The control processing in each of these units will be described below.

[0015] First, the control processing of the variable output unit 20 will be described based on the flow diagram of the control processing of the variable output unit 20 shown in FIG. The variable output unit 20 generates a shape model that models the structure and shape of the structure 1, and sets multiple boundary conditions for this shape model. Here, the load conditions that indicate the applied state of external forces, forced displacements, temperature changes, etc. applied to the structure 1, and the constraint conditions that constrain the displacement of the structure 1 in each direction and in the rotational direction are collectively set as boundary conditions f (step S21). In the process of step S21, the variable output unit 20 assumes various stresses, loads, and constraint conditions that will be applied to the structure 1, and sets multiple boundary conditions fi (i=1, 2, 3,...n).

[0016] Next, the variable output unit 20 selects a boundary condition fi from among the plurality of boundary conditions fi that have been set (step S22). Next, the variable output unit 20 performs a numerical analysis to analyze the structure of the structure 1 based on the selected boundary conditions fi (step S23). In this numerical analysis, the variable output unit 20 sets cross sections 1A and 1B shown in FIG. 5, which are surfaces that provide boundary conditions fi to the shape model. The variable output unit 20 performs the following numerical analysis using the finite element method, for example.

[0017] As shown in Fig. 6A, the variable output unit 20 divides the cross section 1A of the structure 1 into a plurality of rectangular unit surfaces 2, n in the X-axis direction and m in the Y-axis direction, so as to form a grid. Also, as shown in Fig. 6B, the variable output unit 20 divides the cross section 1B into a plurality of rectangular unit surfaces 2, c in the X-axis direction and d in the Y-axis direction, so as to form a grid. Although the unit surface 2 is shown to have a rectangular shape, it is not limited to this and may have, for example, a trapezoidal shape. In this way, if the points where the parallel lines that divide cross sections 1A and 1B into multiple unit surfaces 2 intersect, i.e., the vertices of each unit surface 2, are defined as nodes 2P, then in cross section 1A, each node 2P is represented by a number from (0,0) to (n,m), and in cross section 1B, each node 2P is represented by a number from (0,0) to (c,d).

[0018] The variable output unit 20 then provides the selected boundary conditions fi to each node 2P of the cross sections 1A and 1B, and performs a numerical analysis to predict a deformation vector ui as a first predicted deformation that indicates the deformation that occurs on the measurement surface 1M under these boundary conditions fi.

[0019] In this numerical analysis, the variable output unit 20 sets and stores the boundary condition vector fIi shown in the following (Equation 1), which collectively represents the boundary conditions of each node 2P of these cross sections 1A and 1B as a vector.

[0020]

number

[0021] In setting the boundary condition vector fIi in equation (1) above, for example, at cross section 1A, the boundary condition given to node 2P(0,0) is boundary condition fIi,A(0,0), and the boundary condition given to node 2P(n,m) is boundary condition fIi,A(n,m). Also, at cross section 1B, the boundary condition given to node 2P(0,0) is boundary condition fIi,B(0,0), and the boundary condition given to node 2P(n,m) is boundary condition fIi,B(c,d).

[0022] Next, the variable output unit 20 stores the deformation vector ui of the measurement surface 1M predicted by the above numerical analysis (step S24). The deformation vector ui may be a vector of displacement change amount indicating the difference before and after deformation of each node occurring on the measurement surface 1M.

[0023] Next, the variable output unit 20 performs the numerical analysis shown in steps S22 to S24 for all the boundary conditions fi set in step S21 and determines whether the deformation vector ui has been predicted (step S25). If there are boundary conditions fi that have not been numerically analyzed (step S25, NO), in step S25A, the number i of the boundary conditions fi is incremented by 1, and the processes of steps S22 to S25 are repeated.

[0024] Once the deformation vector ui (i = 1, 2...n) has been found for each of all the set boundary conditions fi (i = 1, 2...n) (step S25, YES), matrices F[fI1,...fIi,...fIn] and U[u1,...ui,...un] are created from the boundary condition vector fIi and the deformation vector ui, and matrix A shown in the following equation (2), which indicates the correlation between matrix F and matrix U, is derived (step S26). Note that matrix F in equation (2) is a pseudo-inverse matrix.

[0025]

number

[0026] Next, the variable output unit 20 derives basis vectors Ur of the linear space of the deformation field of the measurement surface 1M based on the matrix A. The basis vectors Ur may be subjected to singular value decomposition to reduce their rank so that the deformation field can be expressed with the minimum necessary information (step S27).

[0027] Next, the variable output unit 20 derives a coefficient vector fvr as an element constituting the linear space of the deformation field by superposing the basis vector Ur, and a probability distribution of uncertainty including a prediction error of the initial value of this coefficient vector fvr (step S28). Although the coefficient vector fvr is derived as a coefficient for overlapping the basis vector Ur, it may be a scalar instead of a vector.

[0028] In parallel with step S28, the variable output unit 20 acquires a displacement vector u* as a measurement value indicating the actual deformation on the measurement surface 1M of the structure 1, which is input from the measurement unit 10 via the network. Then, the variable output unit 20 derives the probability distribution of the displacement vector u* and the uncertainty ε of this displacement vector u*, which includes measurement errors and the like (step S29).

[0029] Next, the variable output unit 20 outputs, as variables, the derived basis vector Ur, the probability distribution including the uncertainty of the derived coefficient vector fvr, and the probability distribution including the uncertainty of the displacement vector u* (step S30). Each output variable is input to the model generation unit 30 at the subsequent stage.

[0030] Next, the control processing of the model generating unit 30 will be described with reference to the flowchart of the control processing of the model generating unit 30 shown in FIG. First, the model generation unit 30 acquires the basis vector Ur as a variable input from the previous variable output unit 20, a probability distribution including the uncertainty of the coefficient vector fvr, and a probability distribution including the uncertainty ε of the displacement vector u* (step S31).

[0031] Next, the model generating unit 30 determines a first state equation that represents the time transition of the coefficient vector fvr by using the coefficient vector fvr as a first state variable and the uncertainty of its initial value (step S32). As an example, the equation of state when the deformation of a structure does not change over time is shown below (Equation 3). However, the uncertainty of the initial value of the coefficient vector fvr is included in {fvr}.

[0032]

number

[0033] Next, the model generation unit 30 determines a first observation equation that indicates the correlation between the displacement vector u* and the coefficient vector fvr, using the displacement vector u* as a first observation variable (step S33). The first observation equation is shown below (Equation 4).

[0034]

number

[0035] As shown in the above (Equation 4), the displacement vector u* as the first observation variable in the first observation equation is expressed by multiplying the coefficient vector fvr as the first state variable in the above first state equation by the basis vector Ur that constitutes the deformation field.

[0036] Next, the model generating unit 30 generates a first state space model for estimating deformation occurring in the structure 1 using the first state equation and the first observation equation (step S34). The generated first state space model is input to the estimation unit 40 at the subsequent stage.

[0037] Next, the control processing of the estimation unit 40 will be described with reference to the flowchart of the control processing of the estimation unit 40 shown in FIG. First, the estimation unit 40 acquires the first state space model input from the model generation unit 30 at the previous stage (step S41).

[0038] Next, the estimation unit 40 obtains a first prior distribution based on the first state space model (step S42). That is, a probability distribution including uncertainty of the coefficient vector fvr, which is the first state variable in the first state equation, is obtained.

[0039] Next, the estimation unit 40 updates the first prior distribution with the first likelihood function to derive the first posterior distribution (step S43). This first likelihood function is derived from the measurement values ​​measured by the measurement unit 10 as actual deformations of the measurement surface 1M (step S45), as a function showing the likelihood distribution of the displacement vector u* and its error, the uncertainty ε (step S46).

[0040] Next, the estimation unit 40 determines whether the measurement of the measurement surface 1M in step S45 has been performed a set first number of times (step S44). If the number of measurements does not reach the set first number of times (step S44, NO), the posterior distribution derived in step S43 is set as a new prior distribution (step S47).

[0041] After measurements of the measurement surface 1M are performed a set first number of times and the prior distribution is updated using the first likelihood function a set first number of times (step S44, YES), the estimation unit 40 estimates the deformation occurring in the structure 1 based on the updated posterior distribution (step S48). The deformation of the structure 1 can be derived by multiplying the coefficient vector fvr derived based on the posterior distribution by the basis vector Ur.

[0042] Methods for determining the posterior distribution include sequential Bayesian estimation, particle filters, MCMC (Markov chain Monte Carlo), etc. If the deformation to be predicted is elastic and it can be assumed that the measurement error and the system error assumed in the state space model follow a Gaussian distribution, then using a Kalman filter allows for estimation with reduced computational costs.

[0043] Next, the estimation unit 40 outputs the estimated deformation (step S49). The deformation estimated by the estimation unit 40 is input to the output unit 5 shown in FIG. 1 via a network, and is displayed on a display or the like in the output unit 5, thereby notifying the administrator. This allows the administrator to carry out appropriate maintenance and conservation work for the structure 1 based on the estimated deformation.

[0044] Hereinafter, a control process different from the control process of the estimation unit 40 shown in FIG. 4 will be described. FIG. 7 is a flowchart showing another example of the flow of the control process of the estimation unit 40 of this embodiment. Steps S48-A1, S48-A2, and S48-A3 are added to the control processing of the estimation unit 40 shown in Fig. 4. The control operations of steps S41 to S48 are the same as those in Fig. 4, and therefore will not be described here.

[0045] When the deformation of the structure 1 is estimated by multiplying the coefficient vector fvr by the basis vector Ur in step S48, the estimation unit 40 calculates an error, which is the difference between this estimated deformation and the displacement vector u* measured by the measurement unit 10 (step S48-A1). This calculated error is recorded in the estimation unit 40.

[0046] Next, the estimation unit 40 determines whether the calculated error is the smallest value among the recorded past errors (step S48-A2). If the calculated error is not the minimum (step S48-A2, NO), the value of the parameter set as a constant that associates the displacement vector u* and the coefficient vector fvr in the first observation equation (Equation 4) above is changed (step S48-A3). Then, the control process from steps S41 to S48-A1 is performed again using the first state space model configured using the new parameter values, and the estimation unit 40 changes the parameter values ​​until the calculated error reaches a minimum value.

[0047] When the error reaches the minimum value (step S48-A2, YES), the estimation unit 40 outputs the deformation of the structure 1 estimated in step S48 when the error reaches the minimum (step S49). In this way, deformation estimation is performed based on the first state space model in which optimal values ​​are set for the unknown parameters in the first state space model, so that deformation estimation can be performed with reduced error and high accuracy.

[0048] In determining the minimum value of the error in step S48-A2, since no past errors to compare with are recorded when the error is calculated for the first time, the minimum value may not be determined. Instead, the parameters may be changed a set number of times, the error may be calculated each time, and the errors for each parameter may be recorded, after which the minimum value may be determined in step S48-A2. Furthermore, if the displacement vector u* measured in step S45 is recorded, there is no need to measure it again when determining the minimum error value in step S48-A2. The determination of the minimum error in step S48-A2 can be performed using the recorded displacement vector u*.

[0049] In the above, the deformation is estimated based on the first state space model configured using the first observation equation and the first state equation, but it is also possible to estimate the deformation using only the first observation equation. In this case, step S32 for determining the first state equation and step S32 for generating the first state space model are not performed.

[0050] In the above description, the displacement vector u* is used as the first observation variable, and the first observation equation shows the correlation between this displacement vector u* and the coefficient vector fvr as the first state variable. However, this is not limited to this. As the first state variable, a deformation vector u i other than the coefficient vector fvr, or another element constituting the linear space of the deformation field of the measurement surface 1M, such as a basis vector Ur, may be used. That is, at least one of the coefficient vector fvr, the deformation vector u*i, and the basis vector Ur may be used as the first state variable as predicted deformation information indicating a prediction of deformation occurring on the measurement surface of the structure under multiple boundary conditions applied to the structure. The first observation equation may show the correlation between the first observation variable, which is the displacement vector u*, and the predicted deformation information as the first state variable. Furthermore, when a basis vector Ur is used as the first state variable, the basis vector Ur may be represented by a probability distribution.

[0051] Hereinafter, a control process different from the control process of the estimation unit 40 shown in FIG. 4 will be described. FIG. 8 is a flowchart showing another example of the flow of the control process of the estimation unit 40 of this embodiment. Step S48-B1 is added to the control process of the estimation unit 40 shown in Fig. 4. In addition, an imaging device capable of acquiring an image of the measurement surface 1M of the structure 1 is used as the measurement unit 10.

[0052] Here, as a preliminary step of the control processing of the estimation unit 40, when setting the boundary conditions of the variable output unit 20 shown in Fig. 2, at least the longitudinal elastic modulus and Poisson's ratio are set as the material characteristic values ​​of the structure 1 among the longitudinal elastic modulus, density, and Poisson's ratio. The estimation unit 40 estimates the deformation, which will be described below, based on a first state space model using variables reflecting these material characteristic values.

[0053] In step S42, the estimation unit 40 derives at least one of displacement, tilt, and distortion as deformations occurring in the structure 1 and the probability distribution of its error as a first prior distribution based on the first state variable estimated by the first state space model and the probability distribution of its error. In addition, in step S46, the estimation unit 40 derives the probability distribution of at least one of the displacement, inclination, and distortion of the structure 1 and its error based on the image of the measurement surface 1M acquired by the measurement unit 10 as the likelihood distribution of the first likelihood function. For example, distortion can be measured by analyzing and calculating images of the measurement surface 1M before and after deformation, thereby measuring the displacement of the measurement surface 1M and visualizing the distortion.

[0054] In step S43, the estimation unit 40 updates the first prior distribution, which indicates a probability distribution of at least one of displacement, tilt, and distortion, with a first likelihood function, which indicates a probability distribution of at least one of displacement, tilt, and distortion derived from the image, to derive a first posterior distribution.

[0055] Then, the estimation unit 40 judges the soundness of the structure 1 based on the deformation occurring in the structure estimated based on the first posterior distribution (S48-B1). To determine the soundness, the estimation unit 40 determines whether the estimated deformation and the probability distribution of its error exceed a set first threshold value. This first threshold value is set as an index value indicating whether the state of the structure 1 is in a state where soundness can be ensured, and an allowable value is set for at least one of the strain, tilt, and displacement estimated as deformation.

[0056] When strain is set as the first threshold, the estimation unit 40 outputs a probability distribution indicating the distribution of strain as the deformation of the structure estimated based on the first posterior distribution. Because the strain distribution is a differential value of the displacement, it can capture minute displacements and grasp the deformation distribution on the measurement surface 1M in more detail, enabling accurate judgment of the soundness of the structure 1. Furthermore, since the soundness of at least one of the strain, tilt, and displacement estimated as deformation is determined based on a probability distribution, it is possible to output a reliability that takes into account the uncertainty of external forces acting on the structure 1, and the actual soundness of the structure 1 can be quantitatively determined based on the determined soundness.

[0057] The estimation unit 40 outputs the deformation estimated in step S48 and the soundness of the structure 1 determined in step S48-B1 (step S49). This allows the administrator to appropriately perform lifespan assessment, maintenance, conservation work, etc. of the structure 1 based on the output health status.

[0058] The deformation estimation device of this embodiment configured as described above has the following features: 1. A deformation estimation device including a control unit that estimates deformation occurring in a structure based on measurement values ​​indicating deformation on a measurement plane of a surface of the structure, The control unit predicting predicted deformation information that indicates, by a probability distribution, deformations that occur on the measurement surface of the structure under a plurality of boundary conditions that are applied to the structure; setting a first observation equation that indicates a correlation between the predicted deformation information and a first observation variable that indicates the measurement value and an error in the measurement value by a probability distribution, and estimating the deformation that will occur in the structure based on the first observation equation; It is something.

[0059] In this way, the control unit predicts the deformation that occurs on the measurement surface of the structure under multiple boundary conditions, including external forces applied to the structure, as predicted deformation information. That is, the control unit sets multiple boundary conditions and performs a numerical analysis in advance to predict the predicted deformation information for each of the set boundary conditions. The controller then sets a first observation equation that indicates the correlation between the predicted deformation information obtained for each boundary condition and a first observation variable that indicates the deformation obtained from the measured values.The controller then estimates the deformation occurring in the structure based on the first observation equation. That is, the predicted deformation information used as the first state variable in the first observation equation is derived based on the diverse and multiple boundary conditions assumed to be applied to the structure, which enables accurate estimation of the deformation of the structure. Furthermore, since the first state variable is expressed as a probability distribution that includes errors, the value also takes into account the uncertainty of external forces acting on the structure.

[0060] In addition, in the deformation estimation device of this embodiment configured as described above, The control unit using the predicted deformation information as a first state variable, establishing a first state equation that represents a time transition of the first state variable, and establishing a first observation equation that represents a correlation between the first observation variable and the first state variable, and generating a first state space model that estimates deformation of the structure based on the first state equation and the first observation equation; a probability distribution of the first state variable and its error estimated by the first state space model as a first prior distribution, deriving a first likelihood function for the probability distribution of the displacement of the structure and its error obtained from the measurement values, deriving a first posterior distribution based on the first prior distribution and the first likelihood function, and estimating deformation occurring in the structure based on the first posterior distribution; It is something.

[0061] In this way, the variables set as the first state variables in the first state equation are derived based on the diverse and multiple boundary conditions assumed to be applied to the structure, which enables accurate estimation of the deformation of the structure. Furthermore, since the first state variable is expressed as a probability distribution that includes errors, the value also takes into account the uncertainty of external forces acting on the structure.

[0062] Furthermore, a first likelihood function is derived for the probability distribution of deformation of the structure and its error obtained from the measurement values ​​of the measurement unit, a first posterior distribution is derived based on the first prior distribution and the first likelihood function, and deformation occurring in the structure is estimated based on this first posterior distribution. In this way, the first prior distribution is updated using the first likelihood function that indicates the probability distribution of deformation that most closely matches the information included in the first prior distribution, enabling even more accurate deformation estimation.

[0063] In the deformation estimation device of this embodiment configured as described above, The control unit As the first state variable, deriving a first predicted deformation indicating a difference between before and after deformation occurring on the measurement surface under a plurality of boundary conditions applied to the structure, or a probability distribution indicating a correlation between the boundary conditions and the first predicted deformation; It is something.

[0064] In this way, the first state variable may be the deformation vector ui as the first predicted deformation, which is obtained by numerical analysis and indicates the deformation occurring on the measured surface of the structure using a probability distribution, or an element indicating the correlation between this deformation vector ui and the boundary condition fi. By using the deformation vector ui obtained from the measured values ​​or other elements related to this deformation vector ui as the first state variable used in this way, it becomes possible to estimate multifaceted deformation according to the conditions of the structure, boundary conditions, etc., and to estimate deformation with even greater accuracy.

[0065] In the deformation estimation device of this embodiment configured as described above, The control unit deriving a probability distribution of elements constituting a deformation field of the measurement surface with respect to the boundary condition and a probability distribution of errors of the elements as a probability distribution indicating a correlation between the boundary condition and the first predicted deformation; It is something.

[0066] That is, the elements that make up the deformation field of the structure for a variety of boundary conditions are used as elements that indicate the correlation between the boundary conditions and the deformation vector ui, which is the first predicted deformation, and are set as the first state variable. This enables more accurate estimation of the deformation of the structure. Furthermore, because the elements that make up the deformation field are expressed using probability distributions that include errors, the values ​​take into account the uncertainty of external forces acting on the structure, etc.

[0067] In the deformation estimation device of this embodiment configured as described above, The control unit deriving coefficients of each basis vector constituting a linear space in the deformation field as the elements constituting the deformation field; It is something.

[0068] In this way, the coefficients of each basis vector constituting the linear space of the deformation field and the probability distribution of the errors of the coefficients are derived as elements constituting the deformation field of the measurement surface, which are the first state variables in the first state equation. In this way, the probability distribution of the coefficients of each basis vector that constitutes the linear space of the deformation field is set as the first state variable, so the deformation field that occurs in the structure can be accurately represented, and therefore the deformation can be derived with high accuracy.

[0069] In the deformation estimation device of this embodiment configured as described above, The control unit deriving a probability distribution of the first observation variable in the first observation equation by multiplying the first state variable in the first state equation by the basis vector; It is something.

[0070] In this way, the actual displacement of the structure, which is the first observation variable, is expressed by multiplying the coefficient vector, which is the first state variable, by the basis vector. In this way, by minimizing the information included in the prior distribution and expressing the first observation variable as a multiplication of the elements that make up the deformation field, it is possible to improve calculation accuracy while shortening the calculation time for setting the variables used in the first state space model.

[0071] In addition, a Kalman filter is used to estimate the deformation of the structure to derive the first posterior distribution, and measurements are taken multiple times for the deformation of the same structure a set first number of times, and the estimated deformation is updated sequentially in accordance with the measurements, thereby improving the estimation accuracy.

[0072] Embodiment 2 Hereinafter, the second embodiment of the present invention will be described with reference to the drawings, focusing on the differences from the first embodiment. The same parts as those in the first embodiment will be given the same reference numerals and the description thereof will be omitted. FIG. 9 is a block diagram showing a schematic configuration of a deformation estimation device 260 according to the second embodiment and a deformation estimation system 200 including the deformation estimation device 260. As shown in FIG. FIG. 10 is a block diagram showing a schematic configuration of the measurement unit 210 included in the deformation estimation device 260 shown in FIG. FIG. 11 is a flowchart showing the flow of control processing of the variable output unit 270 included in the measurement unit 210 shown in FIG. FIG. 12 is a flowchart showing the flow of control processing of the model generating unit 280 included in the measuring unit 210 shown in FIG. FIG. 13 is a flowchart showing the flow of control processing of the measurement unit 210 shown in FIG. FIG. 14 is a conceptual diagram for explaining the control process of the measuring unit 210 shown in FIG. 10 for the structure 1 to be inspected.

[0073] The deformation estimation system 200 of this embodiment shown in FIG. 9 includes a deformation estimation device 260 and an output unit 5 that outputs the estimation result of this deformation estimation device 260. The deformation estimation device 260 of this embodiment differs from the deformation estimation device 60 of embodiment 1 shown in Fig. 1 in the configuration of the measurement unit 210. The measurement unit 10 of embodiment 1 measures the measurement surface 1M of the structure 1 multiple times from a fixed position. The measurement unit 210 of embodiment 2 measures the measurement surface 1M of the structure 1 multiple times while moving, as shown in Fig. 14, to reduce measurement errors on the measurement surface 1M. It is assumed that the structure 1 does not move. In the second embodiment, the coordinate system of the measurement unit 10 is shown as two-dimensional.

[0074] The configuration of the measurement unit 210 will be described. As shown in Figure 10, the measurement unit 210 includes a measurement device 211 that measures the position of feature point i on the measurement surface 1M of the structure 1 as a measurement value, and a control unit 212 that accurately estimates the position of feature point i on the measurement surface 1M of the structure 1 based on this measurement value. Thus, the deformation estimation device 260 of this embodiment has, as its control section, the control section 50 shown in the first embodiment and the control section 212 provided in the measurement section 210. The control unit 212 includes a variable output unit 270, a model generation unit 280, an estimation unit 290, and an output unit 6. The control processing in each of these units will be described below.

[0075] First, the control processing of the variable output unit 270 will be described based on the flowchart of the control processing of the variable output unit 270 shown in FIG. The variable output unit 270 sets each variable to be used in the model generation unit 280 at the subsequent stage.

[0076] In step S71, the variable output unit 270 sets, as variables, first coordinates (Xw,i, Yw,i, Zw,i), which are the position of feature point i on the measurement plane 1M of the structure 1 in the three-dimensional coordinate system. Here, the first coordinates are defined in the world coordinate system.

[0077] Furthermore, the variable output unit 270 indicates, as a vector indicating conversion information between the first coordinate (Xw,i, Yw,i, Zw,i) in the world coordinate system of the feature point i and the second coordinate (ui, vi) in the two-dimensional coordinate system on the measurement device 211, a quaternion q indicating rotation in the three-dimensional coordinate system and a three-axis vector s indicating parallel translation, as shown in the following (Equation 5), for example, and sets these as variables. As shown in FIG. 14, E is a function that calculates a parameter for rotating coordinates from a quaternion q.

[0078]

number

[0079] Furthermore, the variable output unit 270 sets, as a variable, a movement vector shown in the following equation (6) using the vector of the above conversion information, which indicates the movement speed of the measuring device 211 and its uncertainty.

[0080]

number

[0081] Here, hg and hs indicate the errors that are uncertainties in rotation and speed.

[0082] Next, the variable output unit 270 sets the position (ui, vi) of the feature point i in the two-dimensional coordinate system on the measurement device 211 and the probability distribution of its uncertainty (εui, εvi) as variables (step S72). Next, the variable output unit 270 outputs the derived variables (step S73). Each output variable is input to the model generation unit 280 at the subsequent stage.

[0083] Next, the control processing of the model generating unit 280 will be described with reference to the flowchart of the control processing of the model generating unit 280 shown in FIG. First, the model generation unit 280 acquires the following variables input from the preceding variable output unit 270: the first coordinate of feature point i in the three-dimensional coordinate system; the quaternion q and vector s which are transformation information vectors; the movement vector (d{q} / dt, d{s} / dt) indicating the movement speed of the measurement device 211 and its uncertainty (ηq, ηs); and the position of feature point i in the two-dimensional coordinate system measured by the measurement device 211 and the probability distribution (ui, vi) of its uncertainty (step S81).

[0084] Next, the model generation unit 280 defines the first coordinate of the feature point i in the three-dimensional coordinate system, the quaternion q, the vector s, and the moving speed (d{q} / dt, d{s} / dt) of the measuring device 211 as second state variables, and calculates the second state equations shown in the following equations that represent the time transition of these second state variables (step S82).

[0085]

number

[0086] In the second state equation, the position (first coordinate) of the feature point in the world coordinate system does not change even if the measurement time changes. On the other hand, the rotational and parallel translation vectors (q, s) and the rotational and parallel translation change velocities (d{q} / dt, d{s} / dt) that indicate the movement of the measurement device 211 change over time.

[0087] Next, the model generating unit 280 sets the position of the feature point i in the two-dimensional coordinate system on the measuring device 211 and the probability distributions ui and vi including the uncertainty that is the error therein as second observation variables. The model generation unit 280 then calculates a second observation equation that shows the correlation between this position (ui, vi) and the first coordinate of feature point i in the three-dimensional coordinate system as the second state variables, the quaternion q, the vector s, and the movement speed (d{q} / dt, d{s} / dt) of the measurement device 211 and its uncertainty (ηq, ηs) (step S83). The second observation equation is shown below.

[0088]

number

[0089] Here, hu and hv are functions that convert points in the world coordinate system into points on the two-dimensional coordinate system on the measurement device 211.

[0090] Next, the model generation unit 280 uses the second state equation and the second observation equation to generate a second state space model for estimating the first coordinate, which is the position of the feature point i of the structure 1 on the three-dimensional coordinate system (step S84). The generated second state space model is input to the subsequent estimation unit 290.

[0091] Next, the control processing of the estimation unit 290 will be described with reference to the flowchart of the control processing of the estimation unit 290 shown in FIG. First, the estimation unit 290 acquires the second state space model input from the preceding model generation unit 280 (step S91).

[0092] Next, the estimation unit 290 obtains a second prior distribution based on the second state space model (step S92). That is, a probability distribution is obtained that includes the first coordinates (Xw,i, Yw,i, Zw,i) of the feature point i in the three-dimensional coordinate system, the quaternion q, the vector s, and the moving speeds (d{q} / dt, d{s} / dt) of the measuring device 211 and their uncertainties (ηq, ηs), which are set as second state variables in the second state equation.

[0093] Next, the estimation unit 290 updates the second prior distribution with the second likelihood function to derive the second posterior distribution (step S93). This second likelihood function is derived as a function indicating the position of feature point i in a two-dimensional coordinate system on the measurement device 211 and the probability distribution (ui, vi) of its uncertainty based on the measurement value by measuring feature point i using the measurement device 211 (step S95) (step S96).

[0094] Next, the estimation unit 290 determines whether the measurement of the measurement surface 1M in step S95 has been performed a set second number of times (step S94). If the number of measurements does not reach the set second number of times (step S94, NO), the second posterior distribution derived in step S93 is updated as a new second prior distribution (step S97), and is used as the prior distribution in step S92.

[0095] Methods for calculating the second posterior distribution include sequential Bayesian estimation, particle filters, etc. If the measurement error and the system error assumed in the state space model can be assumed to be Gaussian distributed, the ensemble Kalman filter can be used to reduce the computational cost and make estimations.

[0096] After the measurement of the measurement surface 1M is performed the set second number of times and the prior distribution is updated using the second likelihood function the set second number of times (step S94, YES), the estimation unit 290 estimates and outputs the position of the feature point i of the structure 1 in the world coordinate system based on the updated second posterior distribution (steps S98, S99).

[0097] The first coordinate (Xw,i, Yw,i, Zw,i) of feature point i of structure 1 defined on the world coordinate system will have different values ​​in the second coordinates Co1, Co2 on the two-dimensional coordinate system on the measurement device 211, but in this way, it is possible to accurately estimate the first coordinate on the world coordinate system from the second coordinates Co1, Co2 on the measurement device 211. In this way, the measurement unit 210 derives the position of the feature point i on the structure 1 with high accuracy, with measurement errors and the like reduced. Therefore, the deformation estimation device 260 of this embodiment can accurately estimate the deformation of the structure 1 based on the position information of the feature point i of the structure 1 that has been measured with high accuracy.

[0098] The measuring device 211 for measuring the characteristic points may be a strain gauge, or a device such as a laser displacement meter or an optical interferometer may be used. The measurement device 211 may also measure the feature point i from an image. Measuring the position of the feature point i from an image enables non-contact measurement over a wide area. Furthermore, since multiple points can be measured at once without contact, the measurement time can be shortened. A specific method for measuring the position of feature point i from an image is to use the digital image correlation method. With the digital image correlation method, the measurement device 211 can be made smaller by using only a digital camera.

[0099] Furthermore, if an unsampled Kalman filter is used in the estimation unit 290, measurements are taken multiple times while moving the measurement device with respect to the deformation of the same structure, and the positions of the feature points to be estimated are updated successively in accordance with the measurements, thereby improving the estimation accuracy.

[0100] In the deformation estimation device of this embodiment configured as described above, The control unit a second state equation is set to represent a time transition of a first coordinate of a feature point on the measurement surface of the structure in a three-dimensional coordinate system, a transformation information vector that transforms the first coordinate into a second coordinate in a two-dimensional coordinate system of the measurement unit, and a movement vector using the transformation information vector of the measurement unit that measures the structure while moving within the three-dimensional coordinate system, and a probability distribution of an error therein, as second state variables; and a second observation equation is set to represent a correlation between the second observation variable and the second state variable, as second observation variables, and a second state space model is generated that estimates the first coordinate in the three-dimensional coordinate system of the feature point of the structure based on the second state equation and the second observation equation; a probability distribution of the first coordinate of the feature point in the three-dimensional coordinate system estimated by the second state space model and an error therebetween as a second prior distribution, deriving a second likelihood function for the probability distribution of the second coordinate of the structure measured by the measurement unit in the two-dimensional coordinate system and an error therebetween, deriving a second posterior distribution based on the second prior distribution and the second likelihood function, and estimating the first coordinate of the feature point in the three-dimensional coordinate system of the structure based on the second posterior distribution; It is something.

[0101] In this way, by assuming the movement of the measurement unit when measuring feature points and including this in the second prior distribution in the control unit of the measurement unit, it is possible to reduce measurement errors in the positions of feature points due to positional deviation of the measurement unit.

[0102] Furthermore, for example, when deformation is measured from images taken with a camera fixed to a human hand or a robot, errors occur in the measurement position due to three-dimensional misalignment between the measured images, but the measurement unit 210 of this embodiment can reduce such errors. Furthermore, when capturing images multiple times, there is no need to fix the camera or use multiple cameras to reduce measurement errors. Fixing the camera leads to larger and more complicated equipment, such as the need to create jigs. Using multiple cameras also poses the problem of the equipment becoming larger. According to the measurement unit of this embodiment, even if an administrator photographs a structure while moving around using a single camera without fixing the camera, the error between images can be reduced from correlation information between images with three-dimensional misalignment, and deformation of the structure 1 being photographed can be measured with high accuracy.

[0103] In this way, the deformation estimation device of this embodiment can acquire the displacement of a structure with high accuracy, and therefore can accurately estimate the deformation occurring in the structure.

[0104] Embodiment 3 Hereinafter, the third embodiment of the present invention will be described with reference to the drawings, focusing on the differences from the first embodiment. The same parts as those in the first embodiment will be given the same reference numerals and the description thereof will be omitted. FIG. 15 is a block diagram showing a schematic configuration of a deformation estimation device 360 ​​according to the third embodiment and a deformation estimation system 300 including this deformation estimation device 360. FIG. 16 is a flowchart showing the flow of control processing of deformation estimation device 360 ​​shown in FIG.

[0105] As shown in FIG. 15, a deformation estimation device 360 ​​of this embodiment differs from the deformation estimation device 60 shown in the first embodiment in that it includes a crack estimation unit 341 .

[0106] The control process of the variable output unit 20 differs slightly from that of the first embodiment. When setting the boundary conditions f, which include load conditions indicating the state of external forces, etc., applied to the structure 1 and constraint conditions that constrain the displacement of the structure 1 in each direction and rotational direction, the variable output unit 20 sets an estimated crack inside the structure 1. Specifically, if a crack is assumed to have occurred on the cross section 1A shown in FIG. 5, the variable output unit 20 sets the cross section 1A, which is an area including the location where the crack is expected to occur, as a crack candidate surface. Then, among the multiple nodes 2P on the cross section 1A, the constraint condition for the node 2P that is set to have a crack is set to no constraint on displacement. The variable output unit 20 then performs a numerical analysis based on this boundary condition to determine a deformation vector ui on the measurement surface 1M. Then, the positions of the nodes 2P that are set to have a crack train are sequentially changed, and the deformation vector ui is determined and saved for each node 2P. As a result, the coefficient vector fvr and the basis vector Ur, which are elements constituting the linear space of the deformation field of the measurement surface 1M and which are derived by the variable output unit 20, contain information about the cracks in the cross section 1A.

[0107] Since the coefficient vector fvr and the basis vector Ur, which are variables acquired by the model generation unit 30, contain crack information in this way, this information is reflected in the first space model. Except for the fact that the basis vector Ur and the coefficient vector fvr contain crack information, the first space model is the same as that of the first embodiment.

[0108] As in the first embodiment, the estimation unit 40 estimates and outputs the deformation on the measurement surface 1M (step S49). The crack estimation unit 341 selects constraint conditions that will produce deformation closest to the estimated deformation on the measurement surface 1M, based on the estimated deformation on the measurement surface 1M by the estimation unit 40. That is, since the constraint conditions in the variable output unit 20 include information on cracks occurring at each node 2P on the cross section 1A, the shape of the crack can be obtained from the selected constraint conditions.

[0109] The crack estimation unit 341 outputs the obtained crack information (step S349A-2). The output crack information indicates the shape and position of the crack. The crack information may be displayed numerically or as an image.

[0110] In this way, cross section 1A, which is an area including the location where a crack is predicted to occur inside structure 1, and data on the deformation at measurement surface 1M that occurs when a crack exists at node 2P of cross section 1A are prepared in advance for each node through numerical analysis, and learning data showing the relationship between cross section 1A and measurement surface 1M are prepared.

[0111] In the deformation estimation device of this embodiment configured as described above, The control unit In the constraint conditions constituting the boundary conditions of the numerical analysis, A region including a location where a crack is expected to occur within the structure is set as a crack candidate surface, the crack candidate surface is divided into a plurality of unit surfaces, and among a plurality of nodes constituting each of the unit surfaces, a node that is set as having a crack is set as having no constraint on displacement; predicting the first predicted deformation on the measurement surface by numerical analysis based on the boundary conditions to which the constraint conditions are set; estimating a crack existing on the crack candidate surface based on the constraint condition corresponding to the first predicted deformation and the deformation occurring in the structure estimated based on the first posterior distribution; It is something.

[0112] In this way, cracks occurring inside a structure can be estimated from deformation on the measurement surface, allowing for appropriate lifespan assessments, maintenance, and conservation work to be carried out even on structures that require careful management.

[0113] Embodiment 4 Hereinafter, the fourth embodiment of the present invention will be described with reference to the drawings, focusing on the differences from the first embodiment. The same parts as those in the first embodiment will be assigned the same reference numerals and the description thereof will be omitted. FIG. 17 is a block diagram showing a schematic configuration of a deformation estimation system 400 according to the fourth embodiment. A deformation estimation system 400 of this embodiment is an inspection system for electric power equipment that uses the deformation estimation device 60 of embodiment 1. An electric power equipment connected to a power line that supplies electric power is set as a structure 1 to be inspected. The difference from the deformation estimation system 100 shown in the first embodiment is that the control unit 50 includes a diagnosis unit 407 and a structure control unit 408 .

[0114] Here, as a preliminary step to the control of the estimation unit 40, when setting the boundary conditions, the load conditions indicating the state of application of external forces, etc. applied to the electric power equipment as the structure 1 and the constraint conditions that constrain the displacement of the electric power equipment in each direction and rotational direction are set together as boundary condition f. Furthermore, the material characteristic values ​​include the modulus of longitudinal elasticity, density, and Poisson's ratio of the electric power equipment.

[0115] The estimation unit 40 estimates the deformation based on a first state space model using variables that reflect the material property values. The diagnosing unit 407 judges the soundness and reliability of the electric power equipment based on the deformation occurring in the electric power equipment estimated based on the first posterior distribution.

[0116] To determine the soundness, the diagnosing unit 407 determines whether the probability distribution of the deformation and its error estimated based on the first posterior distribution exceeds a set first threshold value. This first threshold value is set as an index value indicating whether the state of the power equipment is in a state where the soundness can be ensured, and for example, a tolerance value for distortion, tilt, displacement, etc. is set. As a reliability determination, the diagnosing unit 407 determines whether the probability distribution of deformation and its error in the structure estimated based on the first posterior distribution exceeds a second threshold as an index value indicating reliability. As this second threshold, for example, the output, operating temperature, operating period, or operating frequency of the electric power equipment that allows the electric power equipment to be used in a healthy state is set.

[0117] Next, the structure control unit 408 controls the electric power equipment based on the result of the diagnosis unit 407. For example, if it is necessary to reduce the output of the electric power equipment, the structure control unit 408 controls the output to be reduced.

[0118] In the above, the soundness of the electric power equipment was judged based on the deformation on the measurement surface 1M of the electric power equipment, but it is also possible to estimate cracks occurring inside the electric power equipment by configuring it to include the crack estimation unit 341 shown in embodiment 3. For example, the presence or absence of a crack may be determined by setting at least one of a displacement that restrains the electric power equipment when no crack has occurred in the electric power equipment relative to the boundary conditions, or a displacement that restrains the electric power equipment when a crack has occurred in the electric power equipment.

[0119] Furthermore, although the structure 1 is shown as an electric power device connected to a power line, the structure 1 may also be an outdoor building such as a steel bridge or steel tower. In this case, the boundary conditions include the displacement that restrains the steel bridge or tower, the shape of the steel bridge or tower, and the material properties of the steel bridge or tower, such as the modulus of elasticity, density, and Poisson's ratio. In addition, when inspecting for the presence or absence of cracks, the boundary conditions may be set to at least one of constraint conditions in the case where there are no cracks inside the steel bridge or steel tower, or constraint conditions in the case where there are cracks in the steel bridge or steel tower.

[0120] The diagnosis unit 407 may determine the range of use, period of use, frequency of use, or repair range of the railway bridge or tower after the estimation based on the estimated deformation, and may notify the administrator by displaying the range of use, period of use, or repair range of the railway bridge or tower, outputting an alarm if the frequency of use is exceeded, and outputting the timing for repairing the repair range. For example, a drone may be set as the structure control unit 408, the repair area may be output to the drone, and the repair may be performed by the drone.

[0121] The estimated information is used to output the conditions under which a railway bridge or tower can be used in a healthy state, the extent of repairs, the extent of use, period, and extent of repair, as well as the timing and frequency of repairs, and an alarm is output to notify the manager.This allows railway bridges or towers that require careful management to be controlled so that they can be used in a healthy state for a long period of time. Furthermore, since the soundness can be diagnosed using a probability distribution, it is possible to determine the control output for the structure, including the control uncertainty, thereby improving the accuracy of the control.

[0122] Furthermore, when applying this deformation estimation device to pressure vessels and pressure piping used in electric power equipment, the external forces set as boundary conditions may also include internal pressure, and the design pressure or pressure from past inspection records may be set.

[0123] Furthermore, if the structure is electric power equipment, the structure control unit may perform power control to adjust the output of the electric power equipment to be monitored based on the results of the reliability determination. Furthermore, if the structure is electric power equipment such as a power plant, the structure control unit may control the temperature of water, coolant (e.g., hydrogen), etc. supplied to the power plant as environmental elements for adjusting the operating environment of the power plant. Furthermore, the environmental temperature where the electric power equipment is installed and a temperature control device inside the electric power equipment may be controlled as environmental elements for adjusting the operating environment of the electric power equipment.

[0124] In the deformation estimation system of this embodiment configured as described above, a deformation estimation device configured as described above; an output unit that displays an output from the control unit, The control unit performing the numerical analysis by setting at least the longitudinal elastic modulus and the Poisson's ratio of the structure as material property values ​​of the structure in the numerical analysis; performing a soundness determination to determine whether or not the probability distribution of the deformation and error occurring in the structure estimated based on the first posterior distribution exceeds a first threshold value as an index value indicating soundness, and displaying the determination result on the output unit; It is something.

[0125] This allows managers to confirm the integrity of structures based on accurately estimated deformations that occur in the structures, allowing them to carry out appropriate lifespan assessments, maintenance, and conservation work, even for structures that require careful management, enabling long-term operation.

[0126] In addition, in the deformation estimation system of this embodiment configured as described above, the measurement unit acquires an image of the measurement surface of the structure, The control unit deriving, as the first prior distribution, a probability distribution of at least one of displacement, tilt, and strain occurring in the structure and an error therein, based on the first state variable estimated by the first state space model and a probability distribution of an error therein, and deriving, as a likelihood distribution of the first likelihood function, a probability distribution of at least one of displacement, tilt, and strain of the structure based on the image and an error therein; deriving the first posterior distribution based on the first prior distribution and the first likelihood function, and performing the soundness determination based on the first posterior distribution; It is something.

[0127] In addition, in the deformation estimation system of this embodiment configured as described above, The control unit As the structure, an electric power device or an outdoor building connected to a power line that supplies electric power is set, performing the numerical analysis by setting at least the longitudinal elastic modulus and the Poisson's ratio of the electric power equipment or the outdoor building as the material characteristic values ​​in the numerical analysis; performing a reliability determination to determine whether or not the probability distribution of the deformation occurring in the structure and the error thereof estimated based on the first posterior distribution exceeds a second threshold value as an index value indicating reliability, and displaying the determination result on the output unit; It is something.

[0128] In this way, the soundness and reliability of a structure are judged based on deformation estimated by a state space model based on numerical analysis that reflects material property values, so that managers can confirm the soundness of a structure based on the deformation that occurs in the structure, which is accurately estimated, and can therefore perform appropriate lifespan assessments, maintenance, conservation work, etc., even for structures that require careful management. Furthermore, long-term operation is possible by controlling the structure based on the results of this soundness assessment or reliability assessment.

[0129] In addition, in the deformation estimation system of this embodiment configured as described above, The control unit In a configuration in which the electric power equipment is set as the structure, and controlling at least one of power control of the power device and an environmental element that adjusts the operating environment of the power device based on the reliability determination. It is something.

[0130] In this way, based on the results of the reliability assessment, the power control of the power equipment or the environmental factors that adjust the operating environment of the power equipment are adjusted, making it possible to prevent excessive use of the power equipment or ensure an appropriate temperature environment, thereby enabling long-term operation.

[0131] The hardware configuration of the control unit will be described below. Each embodiment includes a processing circuit for executing processing in the control unit. The processing circuit may be dedicated hardware or a CPU (Central Processing Unit, also referred to as a central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, processor, or DSP) that executes a program stored in memory.

[0132] FIG. 18 is a diagram illustrating an example of the hardware configuration of the control unit. 18, the processing circuit 52 is connected to the bus B1. When the processing circuit 52 is dedicated hardware, the processing circuit 52 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, an ASIC, an FPGA, or a combination thereof. Each function of each unit, including the control unit, of the deformation estimation device and deformation estimation system shown in each embodiment may be realized by the processing circuit 52, or the functions of each unit may be realized collectively by the processing circuit 52.

[0133] FIG. 19 is a diagram illustrating an example of the hardware configuration of the control unit. In FIG. 19, a processor 54 and a memory 53, which is a storage device, are connected to a bus B2. When the processing circuit is a CPU, the functions of each unit of the estimation device are realized by software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in the memory 53. The processing circuit realizes the functions of each unit by reading and executing the program stored in the memory 53. That is, the estimation device includes a memory 53 for storing a program whose steps are executed when executed by the processing circuit. Furthermore, these programs can be said to cause a computer to execute a procedure or method to be executed. Here, the memory 404 corresponds to a non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, EEPROM, etc., or a magnetic disk, flexible disk, optical disk, compact disk, minidisk, DVD, etc.

[0134] The functions of each unit of the deformation estimation device may be partially realized by dedicated hardware and partially realized by software or firmware. For example, the model generation unit among the functions may be realized by a processing circuit as dedicated hardware. Furthermore, the processing circuit may read and execute a program stored in memory 53 to realize the diagnosis unit that diagnoses the crack state among the functions.

[0135] Thus, the processing circuitry can implement each of the above functions by hardware, software, firmware, or a combination of these.

[0136] Although the present application describes various exemplary embodiments and examples, the various features, aspects, and functions described in one or more embodiments are not limited to application to a particular embodiment, but may be applied to the embodiments alone or in various combinations. Therefore, countless variations not illustrated are conceivable within the scope of the technology disclosed in this application, including, for example, cases where at least one component is modified, added, or omitted, and cases where at least one component is extracted and combined with a component of another embodiment. [Explanation of symbols]

[0137] 1 Structure, 10,210 Measurement section, 50,212 Control section, 60,260,360 Deformation estimation device, 1M Measurement surface, 100,200,300,400 Deformation estimation system.

Claims

1. 1. A deformation estimation device including a control unit that estimates deformation occurring in a structure based on measurement values ​​indicating deformation on a measurement plane of a surface of the structure, The control unit predicting predicted deformation information that indicates, by a probability distribution, deformations that occur on the measurement surface of the structure under a plurality of boundary conditions that are applied to the structure; a first observation equation indicating a correlation between a first observation variable indicating the measurement value and an error in the measurement value by a probability distribution and the predicted deformation information as a first state variable, and estimating the deformation occurring in the structure based on the first observation equation; Deformation estimation device.

2. The control unit using the predicted deformation information as a first state variable, establishing a first state equation that represents a time transition of the first state variable, and establishing a first observation equation that represents a correlation between the first observation variable and the first state variable, and generating a first state space model that estimates deformation of the structure based on the first state equation and the first observation equation; a probability distribution of the first state variable and its error estimated by the first state space model as a first prior distribution, deriving a first likelihood function for the probability distribution of the displacement of the structure and its error obtained from the measurement values, deriving a first posterior distribution based on the first prior distribution and the first likelihood function, and estimating deformation occurring in the structure based on the first posterior distribution; The deformation estimation device according to claim 1 .

3. The control unit As the first state variable, deriving a first predicted deformation indicating a difference between before and after deformation occurring on the measurement surface under a plurality of boundary conditions applied to the structure, or a probability distribution indicating a correlation between the boundary conditions and the first predicted deformation; The deformation estimation device according to claim 2 .

4. The control unit deriving a probability distribution of elements constituting a deformation field of the measurement surface with respect to the boundary condition and a probability distribution of errors of the elements as a probability distribution indicating a correlation between the boundary condition and the first predicted deformation; The deformation estimation device according to claim 3 . Deformation estimation device.

5. The control unit deriving coefficients of each basis vector constituting a linear space in the deformation field as the elements constituting the deformation field; The deformation estimation device according to claim 4 .

6. The control unit deriving a probability distribution of the first observation variable in the first observation equation by multiplying the first state variable in the first state equation by the basis vector; The deformation estimation device according to claim 5 .

7. The control unit setting the first observation equation including a parameter relating the first observation variable and the first state variable; changing the values ​​of the parameters so that a difference between the deformation of the structure estimated from the first posterior distribution and the deformation of the structure obtained from the measurement values ​​is minimized, and estimating the deformation occurring in the structure; The deformation estimation device according to claim 2 .

8. measuring the measurement value on the measurement surface a set first number of times; the control unit derives the first likelihood function for a probability distribution of the displacement of the structure and an error therein based on the measurement value each time the measurement is performed, and derives the first posterior distribution based on the first prior distribution and the first likelihood function the first number of times. The deformation estimation device according to claim 2 .

9. The control unit a second state equation is set to represent a time transition of a first coordinate of a feature point on the measurement surface of the structure in a three-dimensional coordinate system, a transformation information vector that transforms the first coordinate into a second coordinate in a two-dimensional coordinate system owned by a measurement unit that measures the measurement value, and a movement vector using the transformation information vector of the measurement unit that measures the structure while moving within the three-dimensional coordinate system, and a probability distribution of an error therein, as second state variables; and a second observation equation is set to represent a correlation between the second observation variable and the second state variable, as a second observation variable, to generate a second state space model that estimates the first coordinate in the three-dimensional coordinate system of the feature point of the structure based on the second state equation and the second observation equation; a probability distribution of the first coordinate of the feature point in the three-dimensional coordinate system estimated by the second state space model and an error therebetween as a second prior distribution, deriving a second likelihood function for the probability distribution of the second coordinate of the structure measured by the measurement unit in the two-dimensional coordinate system and an error therebetween, deriving a second posterior distribution based on the second prior distribution and the second likelihood function, and estimating the first coordinate of the feature point in the three-dimensional coordinate system of the structure based on the second posterior distribution; The deformation estimation device according to claim 2 .

10. the measurement unit acquires an image of the measurement surface of the structure, the control unit derives the second coordinates of the feature points in the two-dimensional coordinate system from the image. The deformation estimation device according to claim 9 .

11. the measurement unit measures the feature points a set second number of times while moving relative to the structure; the control unit derives the second likelihood function for the second coordinate in the two-dimensional coordinate system and a probability distribution of an error therebetween based on the measurement value each time the measurement unit performs measurement, and derives the second posterior distribution based on the second prior distribution and the second likelihood function the second number of times. The deformation estimation device according to claim 9 .

12. The control unit In the constraint conditions constituting the boundary conditions, A region including a location where a crack is expected to occur within the structure is set as a crack candidate surface, the crack candidate surface is divided into a plurality of unit surfaces, and among a plurality of nodes constituting each of the unit surfaces, a node that is set as having a crack is set as having no constraint on displacement; predicting the first predicted deformation on the measurement surface based on the boundary condition to which the constraint condition is set; estimating a crack existing on the crack candidate surface based on the constraint condition corresponding to the first predicted deformation and the deformation occurring in the structure estimated based on the first posterior distribution; The deformation estimation device according to claim 3 .

13. A deformation estimation device according to any one of claims 1 to 12; an output unit that displays an output from the control unit, The control unit setting at least the longitudinal elastic modulus and the Poisson's ratio of the structure as material characteristic values ​​of the structure in the prediction of the predicted deformation information; performing a soundness determination to determine whether the estimated deformation occurring in the structure and the probability distribution of the error therein exceed a first threshold value as an index value indicating soundness, and displaying the determination result on the output unit; Deformation estimation system.

14. an image of the measurement surface of the structure is acquired as the measurement value; The control unit using the predicted deformation information as a first state variable, establishing a first state equation that represents a time transition of the first state variable, and establishing a first observation equation that represents a correlation between the first observation variable and the first state variable, and generating a first state space model that estimates deformation of the structure based on the first state equation and the first observation equation; a probability distribution of the first state variable and an error thereof estimated by the first state space model is used as a first prior distribution, a first likelihood function is derived for a probability distribution of the displacement of the structure and an error thereof obtained from the measurement values, a first posterior distribution is derived based on the first prior distribution and the first likelihood function, and a deformation occurring in the structure is estimated based on the first posterior distribution; deriving, as the first prior distribution, a probability distribution of at least one of displacement, tilt, and strain occurring in the structure and an error therein, based on the first state variable estimated by the first state space model and a probability distribution of an error therein, and deriving, as a likelihood distribution of the first likelihood function, a probability distribution of at least one of displacement, tilt, and strain of the structure based on the image and an error therein; deriving the first posterior distribution based on the first prior distribution and the first likelihood function, and performing the soundness determination based on the first posterior distribution; The deformation estimation system of claim 13.

15. The control unit As the structure, an electric power device or an outdoor building connected to a power line that supplies electric power is set, As the material characteristic values, at least the longitudinal elastic modulus and the Poisson's ratio of the electric power equipment or the outdoor building are set, among the longitudinal elastic modulus, density, and Poisson's ratio; performing a reliability determination to determine whether the estimated deformation occurring in the structure and the probability distribution of the error therein exceed a second threshold value as an index value indicating reliability, and displaying the determination result on the output unit; The deformation estimation system of claim 13.

16. The control unit In a configuration in which the electric power equipment is set as the structure, and controlling at least one of power control of the power device and an environmental element that adjusts the operating environment of the power device based on the reliability determination. The deformation estimation system of claim 15.

17. A deformation estimation method using a deformation estimation device including a control unit that estimates deformation occurring in a structure based on measurement values ​​indicating deformation in a measurement plane on a surface of the structure, The control unit a step of predicting predicted deformation information that indicates, by a probability distribution, deformations that occur on the measurement surface of the structure under a plurality of boundary conditions that are applied to the structure; setting a first observation equation that indicates a correlation between a first observation variable that indicates the measurement value and an error in the measurement value by a probability distribution and the predicted deformation information as a first state variable, and estimating the deformation that will occur in the structure based on the first observation equation; Deformation estimation method.

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