WO2018180880A1 - 分析装置、診断装置、分析方法及びコンピュータ読み取り可能記録媒体 - Google Patents
分析装置、診断装置、分析方法及びコンピュータ読み取り可能記録媒体 Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0025—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of elongated objects, e.g. pipes, masts, towers or railways
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0033—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining damage, crack or wear
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0066—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by exciting or detecting vibration or acceleration
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Definitions
- the present invention relates to an analyzer, a diagnostic device, an analysis method, and a computer-readable recording medium.
- Patent Document 1 describes a device deterioration degree prediction method and the like.
- the device deterioration degree prediction method described in Patent Document 1 uses a recurring period based on the statistical distribution of extreme values of measured values of physical characteristics for determining the degree of deterioration obtained at an arbitrary plurality of locations of the device. Estimate the maximum or minimum physical property of the device from the measured value.
- the method for predicting the degree of deterioration of a device described in Patent Document 1 predicts the maximum value of deterioration in the device from a database of the relationship between the degree of deterioration of a previously obtained material and the characteristics.
- Structure failures may be classified into initial failures, sudden failures, and wear failures. For structures in service, consideration must be given to sudden failures and wear failures. And it is preferable that both of these are evaluated in the diagnosis of a structure. That is, for the technique described in Patent Document 1, a technique that enables various diagnoses of structures is required.
- the present invention has been made in order to solve the above-described problems, and has as its main object to provide an analysis apparatus and the like that enable highly accurate diagnosis of a structure state.
- An analysis apparatus shows system identification means for identifying a model representing a time evolution of a structure using a non-Gaussian irregular process based on the response of the structure, and shows variation in input to the structure
- An input modeling means for generating a probability model representing an input distribution based on data, an input generating means for generating an input signal for a structure based on the probability model, and an input signal based on the model and the input signal Response calculating means for obtaining an irregular response of vibration generated in the structure.
- a diagnostic device includes an analysis device, a stress calculation unit that calculates a stress generated in the structure based on a response calculated by a response calculation unit of the analysis device, and a structure based on the stress. And reliability evaluation means for evaluating reliability regarding sudden failure and wear failure.
- the analysis method is based on data indicating a variation of an input to a structure by identifying a model that represents the time evolution of the structure using a non-Gaussian irregular process based on the response of the structure. Generate a probability model representing the distribution of the input, generate an input signal for the structure based on the probability model, and generate an irregular response of the vibration generated in the structure to the input signal based on the model and the input signal. Ask.
- the computer-readable recording medium is a computer-readable recording medium, wherein a process for identifying a model representing a temporal evolution of a structure using a non-Gaussian irregular process based on the response of the structure and an input to the structure.
- a program for executing a process for obtaining an irregular response of vibration generated in the structure is temporarily stored.
- each component of each device represents a functional unit block. Part or all of each component of each device is realized by an arbitrary combination of an information processing device 1000 and a program as shown in FIG. 15, for example.
- the information processing apparatus 1000 includes the following configuration as an example.
- CPU Central Processing Unit
- ROM Read Only Memory
- RAM Random Access Memory
- a storage device 1005 that stores the program 1004
- a drive device 1007 that reads and writes the recording medium 1006
- a communication interface 1008 connected to the communication network 1009 -I / O interface 1010 for inputting / outputting data -Bus 1011 connecting each component
- Each component of each device in each embodiment is realized by the CPU 1001 acquiring and executing a program 1004 that realizes these functions.
- the program 1004 that realizes the function of each component of each device is stored in advance in the storage device 1005 or the RAM 1003, for example, and is read out by the CPU 1001 as necessary.
- the program 1004 may be supplied to the CPU 1001 via the communication network 1009, or may be stored in the recording medium 1006 in advance, and the drive device 1007 may read the program and supply it to the CPU 1001.
- each device may be realized by an arbitrary combination of an information processing device 1000 and a program that are different for each component.
- a plurality of components included in each device may be realized by any combination of one information processing device 1000 and a program.
- each device is realized by a general-purpose or dedicated circuit board including a processor or the like, or a combination thereof. These may be constituted by a single chip cage or may be constituted by a plurality of chip cages connected via a bus. Part or all of each component of each device may be realized by a combination of the above-described circuit and the like and a program.
- each device When some or all of the constituent elements of each device are realized by a plurality of information processing devices and circuits, the plurality of information processing devices and circuits may be centrally arranged or distributedly arranged. Also good.
- the information processing apparatus, the circuit, and the like may be realized as a form in which each is connected via a communication network, such as a client and server system and a cloud computing system.
- the structure is a water pipe that is one of the pipes.
- the structure may be a pipe other than a water pipe, and is not limited to a pipe.
- each device in the following embodiments may target structures other than piping such as water pipes.
- FIG. 1 is a diagram showing an analyzer according to the first embodiment of the present invention.
- the analysis apparatus 100 includes a system identification unit 110, an input modeling unit 120, an input generation unit 130, and a response calculation unit 140.
- the system identification unit 110 identifies a model representing the time evolution of the structure using a non-Gaussian irregular process of the structure.
- the input modeling unit 120 generates a probability model related to the input distribution based on the data indicating the fluctuation of the input to the structure.
- the input generation unit 130 generates an input signal for the structure based on a probability model indicating an input distribution for the structure.
- the response calculation unit 140 obtains an irregular response of vibration generated in the structure with respect to the input signal, based on the model of the structure and the input signal to the structure.
- a diagnostic apparatus 10 having an analysis apparatus 100 is configured.
- the diagnostic device 10 includes an analysis device 100, a stress calculation unit 150, and a reliability evaluation unit 160.
- the stress calculation unit 150 obtains the stress generated in the structure based on the response of the structure.
- the value obtained by the response calculation unit 140 is used.
- the reliability evaluation unit 160 evaluates the reliability of the structure based on the stress generated in the structure. A value obtained by the stress calculation unit 150 is used as the stress.
- the reliability evaluation unit 160 includes a load / strength reliability evaluation unit 161 and a fatigue strength reliability evaluation unit 162.
- the load / strength reliability evaluation unit 161 obtains a load strength reliability indicating the reliability related to the sudden failure of the structure.
- the fatigue strength reliability evaluation unit 162 obtains fatigue strength reliability indicating the reliability related to the wear failure of the structure.
- the overall reliability evaluation unit 163 obtains the reliability of the structure based on the load strength reliability indicating the reliability related to the sudden failure of the structure and the fatigue strength reliability indicating the reliability related to the wear failure.
- the analysis device 100 and the diagnosis device 10 perform analysis and diagnosis using data collected by the data collection unit 180.
- the data collection unit 180 collects data indicating an input to the structure and a response to the input.
- the data collection unit 180 includes, for example, a vibration sensor 181 and a pressure sensor 182.
- the vibration sensor 181 detects vibration propagating in a pipe or a fluid such as water flowing in the pipe.
- an eddy current displacement sensor, a Doppler velocity sensor, a piezoelectric acceleration sensor, or the like is used as the vibration sensor 181, for example, an eddy current displacement sensor, a Doppler velocity sensor, a piezoelectric acceleration sensor, or the like is used.
- Pipes such as water pipes are vibrated due to fluctuations in water pressure or external vibration. That is, vibrations occur in the piping due to fluctuations in water pressure.
- the fluctuation of the water pressure becomes an input to the structure, and the vibration caused by the fluctuation of the water pressure becomes the response of the structure.
- each of the analysis apparatus 100 and the diagnosis apparatus 10 uses a fluctuation in water pressure as an input to the structure, and performs analysis or the like by using a vibration generated due to the fluctuation in water pressure as a response.
- the pressure sensor 182 detects the pressure of the fluid flowing inside the pipe. As described above, pressure fluctuations are input to the structure. When the pipe is a water pipe, the pressure sensor 182 detects the water pressure. As shown in FIG. 3, the vibration sensor 181 is attached to a fire hydrant 502 or the like provided in the pipe 501. Further, the pressure sensor 182 is attached to the pipe 501, for example.
- the structure is a water pipe
- the pressure of the water flowing through the water pipe fluctuates by releasing water or controlling a pump provided in the water pipe.
- the vibration sensor 181 detects a vibration response that is a vibration caused by a change in water pressure.
- the pressure sensor 182 detects water pressure fluctuation.
- each component of the analysis apparatus 100 and the diagnosis apparatus 10 in the present embodiment will be described.
- each component of the analyzer 100 will be described.
- the system identification unit 110 identifies a model representing the time evolution of the structure based on the response of the structure.
- pipes such as water pipes are targeted as structures.
- the system identification unit 110 identifies a structural model representing time evolution using a non-Gaussian irregular process based on a probability model of a response to a structure such as a pipe.
- FIG. 4 shows an example of an assumed structural model.
- FIG. 4A is an example showing a cross section of a pipe to be modeled
- FIG. 4B is an example of a structural model for the pipe.
- the structural model includes a spring and a damper.
- P represents water pressure
- X represents displacement of the vibration response of the structural model
- V represents the speed of vibration response of the structural model.
- the piping to be identified by the structural model shown in FIG. 4B is a cast iron pipe.
- the piping has nonlinear restoring force characteristics.
- a fluctuation in water pressure represented by Gaussian white noise is added as a fluctuation in water pressure as an input to the pipe.
- the target such as the system identification unit 110 and the analysis apparatus 100 including the system identification unit 110 is not limited to such a pipe.
- the input is not limited to Gaussian input.
- the system identification unit 110 treats the equation of motion of the structural model as a non-deterministic stochastic process.
- the system identification unit 110 represents the structural model using the Fokker-Planck equation.
- the Fokker-Planck equation is one of the equations of motion representing the time evolution of a probability density function related to the displacement and speed of vibration of a structure. That is, the system identification unit 110 identifies a model that represents a temporal change related to the probability density function of the vibration displacement and velocity of the structure as a model that represents the time evolution of the structure.
- identification of a structural model based on the following equation (1) is conceivable.
- x 1 represents a random variable for the displacement of the structural model
- x 2 represents a random variable for the velocity of the structural model
- t represents time.
- x 1 corresponds to the X mentioned above
- x 2 corresponds to V described above.
- f ( ⁇ ) represents a probability density function with • as a random variable.
- K is a spring constant in the structural model
- c is a damping coefficient of the damper in the structural model.
- up to a third-order term regarding displacement is considered.
- ⁇ represents a second-order nonlinear coefficient
- ⁇ represents a third-order nonlinear coefficient.
- D represents the diffusion coefficient of the water pressure fluctuation that is an input. D is obtained by obtaining the probability density of the input based on the detection result by the pressure sensor 182 and the like.
- the system identification unit 110 identifies a model based on a moment equation describing the time evolution of the moment with respect to the probability density function.
- E [x] represents a moment with respect to the probability density function f (x).
- equation (2) each of i and j represents the order of the moment.
- Equation (2) is a linear equation for unknown parameters k, c, ⁇ , and ⁇ . Further, the displacement and speed of vibration obtained by the vibration sensor 181 correspond to the response of these structural models to the fluctuation of water pressure as an input. That is, if the probability density function of the response is obtained based on the vibration detected by the vibration sensor 181, the value of the higher order moment in the equation (2) is determined. And the unknown parameter mentioned above is calculated
- Equation (3) The probability density function f (x 1 , x 2 ) of the response is decomposed as in the following equation (3), and the moment is obtained as in the following equation (4).
- ⁇ i represents a parameter parameter of each distribution.
- the nth moment is defined by the following equation (5).
- the probability density function of the response is obtained using an EM (Expectation-Maximization) algorithm using a mixed Gaussian model, a latent distribution estimation algorithm using heterogeneous mixed learning, or the like.
- the system identification unit 110 obtains a moment using a probability density function obtained using these algorithms.
- equation (6) The simultaneous equations for obtaining the unknown parameters described above are expressed as shown in equation (6) as an example.
- equation (6) the fourth moment is taken into consideration.
- equation (6) shows a case where the second-order nonlinear coefficient ⁇ is zero.
- the order of the moment to be considered is not limited to the fourth order. The order of the moment to be considered may be appropriately determined according to various factors such as the type of the target structure.
- the input modeling unit 120 models an input signal based on data indicating input fluctuations with respect to the structure. Specifically, the input modeling unit 120 generates a probability model representing the input distribution.
- the input modeling unit 120 When the structure is a pipe such as a water pipe, the fluctuation of the pressure of fluid such as water flowing inside becomes an input to the pipe. That is, the input modeling unit 120 generates, for example, a probability model of water pressure distribution.
- the input modeling unit 120 generates a probability model representing a water pressure distribution as an input based on data indicating fluctuations in water pressure for several days detected by the pressure sensor 182, for example.
- the data used for generating the probability model is preferably data collected by the pressure sensor 182 over two days or more.
- the data collection period is not particularly limited, and may be determined according to required accuracy, a period during which data can be collected, and the like.
- FIG. 5A shows an example of data indicating fluctuations in the input water pressure.
- the water pressure distribution generally follows a Gaussian distribution, but may have non-Gaussian properties. However, in the input modeling unit 120, the water pressure distribution may be treated as a Gaussian distribution. In the input modeling unit 120, the water pressure distribution may be treated as a non-Gaussian process.
- the input modeling unit 120 generates an input probability model using, for example, a known algorithm based on the data regarding the fluctuation of the water pressure collected as described above.
- an EM algorithm using a mixed Gaussian model, a latent distribution algorithm based on heterogeneous mixed learning, or the like is used.
- FIG. 5B shows an example of a probability model generated by the input modeling unit 120.
- the input generation unit 130 generates an input signal for the structure based on the input probability model for the structure modeled by the input modeling unit 120.
- the input generation unit 130 solves the probability model generated by the input modeling unit 120 with respect to a random variable, and generates an input signal by giving a random number generated using a uniform distribution.
- the generated input signal is, for example, a signal indicating a change in water pressure for a certain time.
- the response calculation unit 140 is configured to correct vibration generated in the structure with respect to the input signal. Find the distribution of rule responses.
- the response calculation unit 140 obtains an irregular response to the input signal using a known numerical integration method such as the Runge-Kutta method. That is, the response calculation unit 140 numerically integrates the input signal that is an irregular signal generated by the input generation unit 130 according to the distribution of the probability model generated by the input modeling unit 120 according to the above-described method. Thus, the distribution of irregular responses of the structure is obtained.
- a model identified based on the parameters k, c, and ⁇ obtained as described above is appropriately used.
- the stress calculation unit 150 obtains the stress generated in the structure such as a pipe based on the irregular response of the structure obtained by the response calculation unit 140.
- U r represents the vertical displacement of the pipe.
- U r corresponds to the distribution of the irregular response of the displacement detected by the vibration sensor 181 and the displacement of the structure obtained by the response calculation unit 140.
- U ⁇ represents the circumferential displacement of the pipe, and the relational expression can be obtained by designating the shape function of the pipe.
- R represents the radius of the pipe
- E represents the elastic modulus of the pipe
- h represents the wall thickness of the pipe
- A represents the cross-sectional area of the pipe
- L represents the length of the pipe.
- A hL holds.
- v represents the Poisson's ratio.
- the reliability evaluation unit 160 evaluates the reliability related to the structure based on the distribution of the stress generated in the structure obtained by the stress calculation unit 150.
- the reliability evaluation unit 160 comprehensively evaluates sudden failures and wear failures related to structures such as piping.
- Structure failures may be explained by reliability theory. In this case, it is necessary to evaluate the reliability of both the sudden failure and the wear failure in the structure in service.
- the reliability related to the sudden failure is expressed using, for example, a known load / strength model.
- the load / strength model is determined based on the strength of the structure and the probability that the load is first applied to the structure.
- the reliability regarding the wear failure is expressed using a load / cycle model or an extreme statistical model.
- the reliability evaluation unit 160 evaluates reliability related to sudden failures and wear failures related to structures such as pipes. And the reliability evaluation part 160 evaluates the reliability regarding the intensity
- the reliability evaluation unit 160 includes a load / strength reliability evaluation unit 161, a fatigue strength reliability evaluation unit 162, and an overall reliability evaluation unit 163.
- the load / strength reliability evaluation unit 161 obtains a load strength reliability. More specifically, the load / strength reliability evaluation unit 161 obtains the load strength reliability of a structure such as a pipe using a load / strength model based on the stress distribution. As the load / strength model, a known model is appropriately used as described above. The reliability obtained by the load / strength reliability evaluation unit 161 is related to the reliability related to the sudden failure. Hereinafter, the reliability expressed by the load / strength reliability evaluation unit 161 is expressed as R 1 .
- the fatigue strength reliability evaluation unit 162 obtains fatigue strength reliability. More specifically, the fatigue strength reliability evaluation unit 162 obtains the fatigue strength reliability of a structure such as a pipe using a method such as a rain flow method based on the stress distribution and the fatigue strength of the structure. The reliability obtained by the fatigue strength reliability evaluation unit 162 is related to the reliability related to wear failure. Hereinafter, representative of the reliability represented by the fatigue reliability evaluation unit 162 and R 2.
- the structure fatigue strength is expressed as a relationship between the number of cycles in which stress is applied to the structure and the magnitude of the amplitude of stress allowed in the number of cycles.
- the fatigue strength reliability evaluation unit 162 decomposes the amplitude and frequency of the stress process, and determines the number of cycles of fatigue strength corresponding to the most frequent stress amplitude in the stress distribution as the lifetime. Find the life of things.
- the overall reliability evaluation unit 163 has a reliability evaluation result regarding the sudden failure obtained by the load / strength reliability evaluation unit 161 and a reliability associated with the wear failure obtained by the fatigue strength reliability evaluation unit 162. The reliability of the structure is evaluated based on the evaluation result.
- a pipe failure may lead to a malfunction even if either a sudden failure or a wear failure occurs.
- a sudden failure or a wear failure in the piping there is a possibility that leakage or the like occurs and replacement is necessary.
- the reliability evaluation unit 160 obtains a reliability in which both a sudden failure and a wear failure are considered in a structure such as a pipe. More specifically, the reliability evaluation unit 160 calculates the reliability using the following equation (10). That is, the reliability evaluation unit 160 sets the product of the two reliability values as a comprehensive reliability in which both sudden failure and wear failure are considered in a structure such as a pipe.
- the input and response of the structure are measured by the data collection unit 180 (step S11).
- the pressure sensor 182 measures fluctuations in water pressure that is input to the pipe
- the vibration sensor 181 measures vibration that is a pipe response.
- the analysis device 100 and the diagnostic device 10 acquire data measured via a wired or wireless communication network, an arbitrary type of recording medium, or the like.
- step S12 The processing from step S12 to step S15 is mainly executed by each element of the analyzer 100.
- the system identification unit 110 performs system identification of a structure (step S12). Details of the processing to be executed will be described later.
- the input modeling unit 120 models the input signal (step S13). In other words, the input modeling unit 120 generates a probability model related to the input signal based on data indicating the water pressure fluctuation detected by the pressure sensor 182 of the data collection unit 180.
- the input generation unit 130 generates an input signal to be added to the structure using the probability model generated in step S13 (step S14).
- step S12 the process of step S12 and the processes of steps S13 and S14, is not limited. These processes may be executed in parallel as in the flowchart shown in FIG. Further, these processes may be executed sequentially in an arbitrary order.
- the response calculation unit 140 calculates an irregular response of the structure (step S15). Based on the input signal to the structure obtained in step S14 and the model of the structure generated in step S12, the response calculation unit 140 calculates a distribution of irregular responses using a known method or the like. . In step S ⁇ b> 15, the response calculation unit 140 obtains at least a response distribution for the displacement.
- the stress calculation unit 150 calculates the distribution of stress generated in the structure based on the irregular response obtained in step S15 (step S16).
- the stress calculation unit 150 obtains a stress distribution mainly based on the displacement distribution obtained in step S15.
- the reliability evaluation unit 16 evaluates the reliability of the structure.
- the load / strength reliability evaluation unit 161 evaluates the load / strength reliability (step S17). That is, the load / strength reliability evaluation unit 161 evaluates the reliability related to the sudden failure.
- the fatigue strength reliability evaluation unit 162 evaluates the fatigue strength reliability (step S18). That is, the fatigue strength reliability evaluation unit 162 evaluates the reliability related to wear failure.
- step S17 and the process of step S18 is not limited. These processes may be executed in parallel as in the flowchart shown in FIG. Alternatively, these processes may be executed sequentially in an arbitrary order.
- the overall reliability evaluation unit 163 evaluates the reliability of the structure based on the load / strength reliability obtained in step S17 and the fatigue strength reliability obtained in step S18. (Step S19).
- step S12 is executed in detail according to the flowchart shown in FIG. With reference to the flowchart shown in FIG. 8, the operation
- the system identification unit 110 acquires vibration data measured by the vibration sensor 181 of the data collection unit 180 in a structure that is a system identification target (step S101).
- the system identification unit 110 estimates the probability density function of the response based on the vibration data measured in step S101 (step S102).
- the system identification unit 110 calculates a moment related to the probability density function using the probability density function of the response estimated in step S102 (step S103). In this case, the system identification unit 110 calculates a high-order moment up to a predetermined order. In addition, the system identification unit 110 may accept a designation relating to the maximum order of the high-order moment.
- the system identification unit 110 calculates parameters related to the above-described structural model (step S104).
- the structural model of the structure is identified by calculating the parameters.
- the analysis apparatus 100 identifies a model representing the time evolution of a structure using a non-Gaussian irregular process based on the distribution of the response of the structure. Then, the analysis apparatus 100 obtains a response to an input applied to the structure and a stress generated in the structure as a distribution based on the model representing the time evolution.
- the analyzer 100 a non-Gaussian model is identified. Therefore, the above-described problem can be avoided.
- the sudden failure is represented by the load / strength model, the evaluation of the stress and the strength are realized as described above, so that the reliability of the sudden failure can be evaluated.
- the analyzer 100 it is possible to accurately evaluate the reliability in consideration of not only the wear failure but also the sudden failure. That is, the analysis apparatus 100 and the diagnostic apparatus 10 enable highly accurate diagnosis about the state of the structure.
- FIG. 9 shows a configuration of the diagnostic apparatus 11 in the modification of the embodiment described above. As illustrated in FIG. 9, the diagnostic device 11 is different from the diagnostic device 10 in that the diagnostic device 11 includes an intensity estimation unit 170 as compared with the diagnostic device 10 described above.
- the strength estimation unit 170 estimates the strength of the structure when data is collected based on the vibration of the structure detected by the vibration sensor 181.
- the strength estimation unit 170 uses known methods as appropriate to obtain the estimated tensile strength and estimated fatigue strength of the structure when data is collected.
- the intensity estimation unit 170 obtains a distribution of these intensity, for example.
- the load / strength reliability evaluation unit 161 is based on the estimated tensile strength distribution of the structure estimated by the strength estimation unit 170 and the stress distribution obtained by the stress calculation unit 150. Evaluate the load and strength reliability of the structure.
- the fatigue strength reliability evaluation unit 162 determines the fatigue of the structure based on the estimated fatigue strength distribution of the structure estimated by the strength estimation unit 170 and the stress distribution obtained by the stress calculation unit 150. Evaluate strength reliability.
- the diagnosis device 11 has the same effect as the diagnosis device 10.
- Example 10 The above-described diagnostic device 10 or the like is applied to the diagnosis of the reliability of the structure.
- an experiment for diagnosing reliability was performed on a shared water pipe.
- a water pipe a normal gray cast iron pipe having a diameter of 100 mm (millimeter) and a length of 5 m (meter) was used.
- the water pipe to be tested was passed through a test rig.
- the water pipes to be tested had fire hydrants at both ends and the ends were closed. There was no flow in the water inside the water pipe.
- a pressure pump was installed upstream of the water pipe.
- a dynamic water pressure exciter was installed in the fire hydrant on the upstream side.
- An eddy current displacement sensor and a laser Doppler vibration velocimeter were installed as a vibration sensor 181 in the downstream fire hydrant.
- a dynamic water pressure sensor was installed as a pressure sensor 182 in the downstream fire hydrant.
- the hydrostatic pressure of the water passed through the inside was set to 0.6 MPa (megapascal), and the water pipe was vibrated by generating a white noise series using a hydrodynamic vibrator. .
- the vibration response and the water pressure response with respect to the vibration by a dynamic water pressure vibrator were measured by each sensor mentioned above.
- the vibration response was measured under the conditions of a measurement range of ⁇ 10 V (volts), an AD (Analog-to-digital) conversion bit number of 16 bits, and a sampling frequency of 3 kHz. The measurement was performed for 5000 seconds.
- the system identification unit 110 Based on the data measured by the eddy current displacement sensor and the laser Doppler vibrometer, the system identification unit 110 obtains a probability density function of the vibration response. In this case, the system identification unit 110 adapted the vibration response to a mixed Gaussian model of the third order. The system identification unit 110 uses an EM algorithm for estimating the probability density function. And the convergence of the estimated value of the parameter parameter was confirmed by repeating 25 steps.
- FIG. 10 shows an example in which the vibration response is fitted to a third-order mixed Gaussian model. That is, FIG. 10A shows the measured vibration response displacement, and FIG. 10B shows the probability density function for the displacement distribution estimated based on the measured value. FIG. 10C shows the speed of the measured vibration response, and FIG. 10D shows the probability density function for the velocity distribution estimated based on the measured value.
- FIG. 11 shows the displacement restoring force characteristics identified by the system identification unit 110.
- a dotted line indicates a true value which is an actual value obtained by the sensor, and a solid line indicates a value obtained using the identified model.
- the input modeling unit 120 performs probability modeling of the water pressure distribution as an input.
- data of the water pressure fluctuation data for two days measured in a cast iron pipe in actual operation with the same diameter and the same material was used.
- the EM algorithm is used for the univariate Gaussian distribution in the modeling. It was confirmed that the estimation of the parameter parameter converged by repeating the steps five times.
- FIG. 12 shows an example of a water pressure distribution and a probability model.
- FIG. 12A shows the measured water pressure distribution.
- FIG. 12B shows an example of a probability model estimated by the input modeling unit 120 based on the water pressure distribution shown in FIG.
- the stress calculation unit 150 obtained the stress generated in the water pipe based on the response obtained by the response calculation unit 140.
- the reliability evaluation unit 160 evaluated the reliability.
- the reliability evaluation unit 160 when evaluating the reliability, “Jesson DA, Mohebbi H, Farrow J, Mulheron MJ, Smith PA. (2013)“ On the condition assessment of cast iron trunk main: The effect of microstructure and The values of tensile strength described in “in-service“ graphitisation ”on“ mechanical properties ”in“ flexure ”.“ Materials ”Science“ and ”Engineering“ A, ”576,“ pp. ”192-201.” were used.
- FIG. 13 shows the result of determining the relationship between the stress and the tensile strength obtained by comparing with the value of the tensile strength described in the above document.
- Reliability R obtained is obtained as a value smaller than the load-strength reliability R 1. Therefore, it was confirmed by the diagnostic device 10 that the reliability is evaluated on the safe side. In other words, it was confirmed that the reliability was evaluated so as to reduce the possibility that the water pipe could stop functioning due to a failure.
- the structural model identified by the system identification unit 110 was evaluated for reliability in the case of further simulating deterioration.
- the value of the spring constant k included in the structural model obtained by the system identification unit 110 is changed by 5% (percent).
- An example of the structural model in this case is shown in FIG.
- the response calculation unit 140 obtains the response of the water pipe
- the stress calculation unit 150 obtains the stress generated in the water pipe based on the response.
- the reliability evaluation part 160 evaluated reliability based on the stress calculated
- the obtained reliability R value is smaller than the reliability R in the previous example. That is, it was confirmed that the diagnostic device 10 appropriately evaluates the degree of deterioration according to the state of deterioration.
- the reliability R was determined as a value smaller than the load / strength reliability R 1 . Therefore, as in the previous example, it was confirmed that the reliability was evaluated by the diagnostic device 10 on the safe side. In other words, it was confirmed that the reliability was evaluated so as to reduce the possibility of overlooking that the water pipe could stop functioning due to a failure even when simulating deterioration.
- System identification means for identifying a model representing the time evolution of the structure using a non-Gaussian irregular process based on the distribution of the response of the structure;
- Input modeling means for generating a probability model representing the distribution of the input based on data indicating fluctuations in the input to the structure;
- Input generating means for generating an input signal for the structure based on the probability model;
- response calculating means for obtaining an irregular response of vibration generated in the structure with respect to the input signal;
- Appendix 2 The analysis apparatus according to appendix 1, wherein the system identification unit identifies the model based on a probability density function of vibration displacement and velocity as the response.
- Appendix 3 The analysis apparatus according to appendix 2, wherein the system identification unit identifies the model representing time evolution of the probability density function of the vibration displacement and velocity.
- Appendix 4 The analysis apparatus according to appendix 2 or 3, wherein the system identification unit identifies the model based on a moment about the probability density function of the vibration displacement and velocity.
- Appendix 5 The analyzer according to any one of appendices 1 to 4, wherein the structure is a pipe.
- Appendix 6 The analysis apparatus according to appendix 5, wherein the input modeling unit generates the probability model indicating a pressure distribution based on a variation in pressure of a fluid flowing through the pipe collected during a predetermined period.
- Appendix 7 The analyzer according to appendix 6, wherein the input generation means generates the input signal representing the pressure fluctuation based on the probability model.
- the reliability evaluation means includes Load strength reliability evaluation means for evaluating load strength reliability indicating reliability related to the sudden failure of the structure; Fatigue strength reliability evaluation means for evaluating fatigue strength reliability indicating reliability related to wear failure of the structure; An overall reliability evaluation means for evaluating the reliability of the structure based on the load strength reliability and the fatigue strength reliability; The diagnostic device according to appendix 9.
- the load strength reliability evaluation means evaluates the load strength reliability based on the stress and the relationship between the stress and the strength of the structure.
- the diagnostic apparatus according to appendix 10.
- the overall reliability evaluation means evaluates the fatigue strength reliability based on the stress and fatigue strength of the structure.
- the diagnostic apparatus according to appendix 10.
- the overall reliability evaluation means calculates the reliability of the structure based on a product of the load strength reliability and the fatigue strength reliability.
- the diagnostic device according to any one of appendices 10 to 12.
- Diagnosis apparatus 100 Analysis apparatus 110 System identification part 120 Input modeling part 130 Input generation part 140 Response calculation part 150 Stress calculation part 160 Reliability evaluation part 161 Load / strength reliability evaluation part 162 Fatigue strength reliability evaluation part 163 Total reliability Degree evaluation unit 170 Strength estimation unit 180 Data collection unit 181 Vibration sensor 182 Pressure sensor
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Abstract
Description
・ROM(Read Only Memory)1002
・RAM(Random Access Memory)1003
・RAM1003にロードされるプログラム1004
・プログラム1004を格納する記憶装置1005
・記録媒体1006の読み書きを行うドライブ装置1007
・通信ネットワーク1009と接続する通信インターフェース1008
・データの入出力を行う入出力インターフェース1010
・各構成要素を接続するバス1011
各実施形態における各装置の各構成要素は、これらの機能を実現するプログラム1004をCPU1001が取得して実行することで実現される。各装置の各構成要素の機能を実現するプログラム1004は、例えば、予め記憶装置1005やRAM1003に格納されており、必要に応じてCPU1001が読み出す。なお、プログラム1004は、通信ネットワーク1009を介してCPU1001に供給されてもよいし、予め記録媒体1006に格納されており、ドライブ装置1007が当該プログラムを読み出してCPU1001に供給してもよい。
まず、本発明の第1の実施形態について説明する。図1は、本発明の第1の実施形態における分析装置を示す図である。
上述した実施形態に示す分析装置100及び診断装置10には、変形例が考えられる。以下に変形例の一部を示す。
上述した診断装置10等を、構造物の信頼度の診断へ適用した。本実施例では、共用後の水道管を対象として、信頼度を診断する実験を行なった。水道管として、口径が100mm(ミリメートル)、長さが5m(メートル)の普通ねずみ鋳鉄管が用いられた。供試される水道管に対して、試験用リグによって通水した。
構造物の応答の分布に基づいて、非ガウス不規則過程を用いて前記構造物の時間発展を表すモデルを同定するシステム同定手段と、
前記構造物に対する入力の変動を示すデータに基づいて、前記入力の分布を表す確率モデルを生成する入力モデル化手段と、
前記確率モデルに基づいて、前記構造物に対する入力信号を生成する入力生成手段と、
前記モデル及び前記入力信号に基づいて、前記入力信号に対して前記構造物に生じる振動の不規則応答を求める応答算出手段と、
を備える分析装置。
前記システム同定手段は、前記応答である振動の変位及び速度の確率密度関数に基づいて前記モデルを同定する、付記1に記載の分析装置。
前記システム同定手段は、前記振動の変位及び速度の前記確率密度関数の時間発展を表す前記モデルを同定する、付記2に記載の分析装置。
前記システム同定手段は、前記振動の変位及び速度の前記確率密度関数についてのモーメントに基づいて前記モデルを同定する、付記2又は3に記載の分析装置。
前記構造物は配管である、付記1から4のいずれか一項に記載の分析装置。
前記入力モデル化手段は、所定の期間に収集された前記配管を流れる流体の圧力の変動に基づいて、圧力の分布を示す前記確率モデルを生成する、付記5に記載の分析装置。
前記入力生成手段は、前記確率モデルに基づいて、前記圧力の変動を表す前記入力信号を生成する、付記6に記載の分析装置。
前記応答算出手段は、前記入力信号が表す前記圧力の変動に対して前記構造物に生じる振動の不規則応答を求める、付記7に記載の分析装置。
付記1から8のいずれか一項に記載の分析装置と、
前記分析装置の応答算出手段によって算出された前記応答に基づいて、前記構造物に生じる応力を算出する応力算出手段と、
前記応力に基づいて、前記構造物の突発故障及び磨耗故障を含む信頼度を評価する信頼度評価手段とを備える、
診断装置。
前記信頼度評価手段は、
前記構造物の突発故障に関する信頼度を示す負荷強度信頼度を評価する負荷強度信頼度評価手段と、
前記構造物の磨耗故障に関する信頼度を示す疲労強度信頼度を評価する疲労強度信頼度評価手段と、
前記負荷強度信頼度及び前記疲労強度信頼度に基づいて前記構造物の信頼度を評価する総合信頼度評価手段とを含む、
付記9に記載の診断装置。
前記負荷強度信頼度評価手段は、前記応力及び前記応力と前記構造物の強度との関係に基づいて、前記負荷強度信頼度を評価する、
付記10に記載の診断装置。
前記総合信頼度評価手段は、前記応力及び前記構造物の疲労強度に基づいて、前記疲労強度信頼度を評価する、
付記10に記載の診断装置。
前記総合信頼度評価手段は、前記負荷強度信頼度及び前記疲労強度信頼度との積に基づいて、前記構造物の前記信頼度を求める、
付記10から12のいずれか一項に記載の診断装置。
構造物の応答に基づいて、非ガウス不規則過程を用いて前記構造物の時間発展を表すモデルを同定し、
前記構造物に対する入力の変動を示すデータに基づいて、前記入力の分布を表す確率モデルを生成し、
前記確率モデルに基づいて、前記構造物に対する入力信号を生成し、
前記モデル及び前記入力信号に基づいて、前記入力信号に対して前記構造物に生じる振動の不規則応答を求める、
分析方法。
コンピュータに、
構造物の応答に基づいて、非ガウス不規則過程を用いて前記構造物の時間発展を表すモデルを同定する処理と、
前記構造物に対する入力の変動を示すデータに基づいて、前記入力の分布を表す確率モデルを生成する処理と、
前記確率モデルに基づいて、前記構造物に対する入力信号を生成する処理と、
前記モデル及び前記入力信号に基づいて、前記入力信号に対して前記構造物に生じる振動の不規則応答を求める処理と、
を実行させるプログラム。
100 分析装置
110 システム同定部
120 入力モデル化部
130 入力生成部
140 応答算出部
150 応力算出部
160 信頼度評価部
161 負荷・強度信頼度評価部
162 疲労強度信頼度評価部
163 総合信頼度評価部
170 強度推定部
180 データ収集部
181 振動センサ
182 圧力センサ
Claims (15)
- 構造物の応答の分布に基づいて、非ガウス不規則過程を用いて前記構造物の時間発展を表すモデルを同定するシステム同定手段と、
前記構造物に対する入力の変動を示すデータに基づいて、前記入力の分布を表す確率モデルを生成する入力モデル化手段と、
前記確率モデルに基づいて、前記構造物に対する入力信号を生成する入力生成手段と、
前記モデル及び前記入力信号に基づいて、前記入力信号に対して前記構造物に生じる振動の不規則応答を求める応答算出手段と、
を備える分析装置。 - 前記システム同定手段は、前記応答である振動の変位及び速度の確率密度関数に基づいて前記モデルを同定する、請求項1に記載の分析装置。
- 前記システム同定手段は、前記振動の変位及び速度の前記確率密度関数の時間発展を表す前記モデルを同定する、請求項2に記載の分析装置。
- 前記システム同定手段は、前記振動の変位及び速度の前記確率密度関数についてのモーメントに基づいて前記モデルを同定する、請求項2又は3に記載の分析装置。
- 前記構造物は配管である、請求項1から4のいずれか一項に記載の分析装置。
- 前記入力モデル化手段は、所定の期間に収集された前記配管を流れる流体の圧力の変動に基づいて、圧力の分布を示す前記確率モデルを生成する、請求項5に記載の分析装置。
- 前記入力生成手段は、前記確率モデルに基づいて、前記圧力の変動を表す前記入力信号を生成する、請求項6に記載の分析装置。
- 前記応答算出手段は、前記入力信号が表す前記圧力の変動に対して前記構造物に生じる振動の不規則応答を求める、請求項7に記載の分析装置。
- 請求項1から8のいずれか一項に記載の分析装置と、
前記分析装置の応答算出手段によって算出された前記応答に基づいて、前記構造物に生じる応力を算出する応力算出手段と、
前記応力に基づいて、前記構造物の突発故障及び磨耗故障を含む信頼度を評価する信頼度評価手段とを備える、
診断装置。 - 前記信頼度評価手段は、
前記構造物の突発故障に関する信頼度を示す負荷強度信頼度を評価する負荷強度信頼度評価手段と、
前記構造物の磨耗故障に関する信頼度を示す疲労強度信頼度を評価する疲労強度信頼度評価手段と、
前記負荷強度信頼度及び前記疲労強度信頼度に基づいて前記構造物の信頼度を評価する総合信頼度評価手段とを含む、
請求項9に記載の診断装置。 - 前記負荷強度信頼度評価手段は、前記応力及び前記応力と前記構造物の強度との関係に基づいて、前記負荷強度信頼度を評価する、
請求項10に記載の診断装置。 - 前記総合信頼度評価手段は、前記応力及び前記構造物の疲労強度に基づいて、前記疲労強度信頼度を評価する、
請求項10に記載の診断装置。 - 前記総合信頼度評価手段は、前記負荷強度信頼度及び前記疲労強度信頼度との積に基づいて、前記構造物の前記信頼度を求める、
請求項10から12のいずれか一項に記載の診断装置。 - 構造物の応答に基づいて、非ガウス不規則過程を用いて前記構造物の時間発展を表すモデルを同定し、
前記構造物に対する入力の変動を示すデータに基づいて、前記入力の分布を表す確率モデルを生成し、
前記確率モデルに基づいて、前記構造物に対する入力信号を生成し、
前記モデル及び前記入力信号に基づいて、前記入力信号に対して前記構造物に生じる振動の不規則応答を求める、
分析方法。 - コンピュータに、
構造物の応答に基づいて、非ガウス不規則過程を用いて前記構造物の時間発展を表すモデルを同定する処理と、
前記構造物に対する入力の変動を示すデータに基づいて、前記入力の分布を表す確率モデルを生成する処理と、
前記確率モデルに基づいて、前記構造物に対する入力信号を生成する処理と、
前記モデル及び前記入力信号に基づいて、前記入力信号に対して前記構造物に生じる振動の不規則応答を求める処理と、
を実行させるプログラムを格納したコンピュータ読み取り可能記録媒体。
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