Methods for assessing remaining life of pressure vessels

Abstract

To provide an evaluation method for the remaining life of a pressure vessel more accurately.SOLUTION: The method includes a step for actually measuring two or more degradation indices indicating the degradation state of each pressure vessel by performing non-destructive inspection on a plurality of pressure vessels, a step for actually measuring a remaining life index indicating the remaining life of each pressure vessel by performing a destructive inspection, a step for creating a calculation model to estimate the remaining life index from the two or more degradation indices by statistical analysis or machine learning using the two or more degradation indices and the remaining life index, a step for actually measuring two or more degradation indices for a pressure vessel by performing nondestructive inspection for the pressure vessel to be evaluated, and a step for calculating the remaining life index of the pressure vessel using the calculation model from the two or more degradation indices of the pressure vessel to be evaluated. The inspection includes at least a first non-destructive inspection using radiography and a second non-destructive inspection using an acoustic emission measurement with a filling cycle.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The technology disclosed in this specification relates to a method for assessing the remaining life of a pressure vessel. [Background technology]

[0002] Patent document 1 discloses a method for determining that a pressure vessel (referred to as a high-pressure tank in Patent document 1) has exceeded its useful life when the number of times the pressure vessel has been filled and released with gas reaches a preset value. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2011-163489 A Summary of the Invention [Problem to be solved by the invention]

[0004] The deterioration of a pressure vessel varies depending on the environment in which the pressure vessel is used. For example, when a pressure vessel is used in an area with relatively large temperature changes, even if the number of times the pressure vessel is filled and discharged does not reach a predetermined value, the pressure vessel may have deteriorated beyond the deterioration corresponding to the use of its useful life. For example, when a pressure vessel is used in an area with relatively small temperature changes, even if the number of times the pressure vessel is filled and discharged reaches a predetermined value, the pressure vessel may not have deteriorated to the extent corresponding to the use of its useful life. In this way, when the remaining life of a pressure vessel is evaluated based on the number of times it is filled and discharged, it may differ from the remaining life estimated from the actual deterioration state of the pressure vessel. This specification provides a technology for evaluating the remaining life of a pressure vessel more accurately than the conventional technology for evaluating the remaining life of a pressure vessel based on the number of times it is filled. [Means for solving the problem]

[0005] This specification discloses a method for evaluating the remaining life of a pressure vessel. The method includes the steps of: measuring two or more deterioration indices indicating the deterioration state of each pressure vessel by performing non-destructive testing on a plurality of pressure vessels; measuring a remaining life index indicating the remaining life of each pressure vessel by performing destructive testing on each pressure vessel after the non-destructive testing; creating a calculation model for estimating the remaining life index from the two or more deterioration indices by statistical analysis or machine learning using the two or more deterioration indices and the remaining life index measured on the plurality of pressure vessels; measuring the two or more deterioration indices for the pressure vessel to be evaluated by performing non-destructive testing on the pressure vessel; and calculating the remaining life index of the pressure vessel from the two or more deterioration indices measured on the pressure vessel to be evaluated using the calculation model. The non-destructive testing includes at least a first non-destructive testing using X-ray photography and a second non-destructive testing using acoustic emission measurement involving a filling cycle.

[0006] In the above-mentioned method, a computation model is created by statistical analysis or machine learning using two or more degradation indices indicating the degradation state of the pressure vessel measured by performing non-destructive testing and a remaining life index indicating the remaining life of the pressure vessel measured by performing destructive testing, to estimate a remaining life index from the two or more degradation indices. Furthermore, the non-destructive testing includes at least a first non-destructive testing using X-ray photography and a second non-destructive testing using acoustic emission (hereinafter simply referred to as AE) measurement accompanied by a filling cycle. Therefore, the computation model created by the method disclosed in the present specification can estimate a remaining life index of the pressure vessel by utilizing information obtained by using X-ray photography and information obtained by using AE measurement, which are actually measured for the pressure vessel to be evaluated. With this configuration, the remaining life of the pressure vessel to be evaluated can be more accurately evaluated based on each remaining life index measured by destructive testing of a plurality of pressure vessels, without actually destroying the pressure vessel to be evaluated.

[0007] Details and further improvements of the technology disclosed in this specification are described in the following "Forms for Carrying Out the Invention". [Brief description of the drawings]

[0008] [Figure 1] 1 shows a schematic diagram of an evaluation device 10 for carrying out a life expectancy evaluation method according to an embodiment. [Diagram 2] A flow diagram of the process executed by the control device 20 to create the computational model 24 is shown. [Diagram 3] An example of data input to the computation model 24 of the learning device 22 is shown. [Figure 4] 1 shows a flow diagram of a process executed by the control device 20 to estimate the remaining life of a pressure vessel to be evaluated. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] FIG. 1 shows a schematic diagram of an evaluation device 10. The evaluation device 10 is a device that performs a remaining life evaluation of a pressure vessel. The evaluation device 10 also performs various inspections on a plurality of pressure vessels. The evaluation device 10 includes a compressor 2, a pressure sensor 4, an X-ray irradiation device 6, an X-ray imaging device 7, an AE (short for Acoustic emission) sensor 8, a control device 20, a learning device 22, and three valves 12, 14, and 16.

[0010] The compressor 2 is connected to the pressure vessel T1 via the valves 12, 14 and the pressure sensor 4. The compressor 2 is a device that fills the pressure vessel T1 with high-pressure gas (e.g., air). When the compressor 2 fills the pressure vessel T1 with high-pressure gas while the valves 12, 14 are open and the valve 16 is closed, the internal pressure of the pressure vessel T1 increases. When the compressor 2 is turned off, the valve 12 is closed, and the valve 16 is opened while the internal pressure of the pressure vessel T1 increases, the high-pressure gas in the pressure vessel T1 is discharged from the pressure vessel T1. As a result, the internal pressure of the pressure vessel T1 decreases. In this way, the compressor 2 and the valves 12, 14, 16 fill the pressure vessel T1 with high-pressure gas and release the high-pressure gas from the pressure vessel T1.

[0011] The pressure sensor 4 detects the pressure of the pipe connecting the compressor 2 and the pressure vessel T1. That is, the pressure sensor 4 measures the internal pressure of the pressure vessel T1. The pressure sensor 4 transmits the detected pressure value to the control device 20.

[0012] The X-ray irradiator 6 irradiates X-rays towards the pressure vessel T1. The X-ray imaging device 7 faces the X-ray irradiator 6 across the pressure vessel T1. The X-ray imaging device 7 has a number of X-ray detection elements. X-rays irradiated from the X-ray irradiator 6 pass through the pressure vessel T1 and enter the X-ray imaging device 7. The X-ray detection elements of the X-ray imaging device 7 detect the intensity of the X-rays entering the X-ray imaging device 7.

[0013] For example, when a gap is formed in the side wall of the pressure vessel T1, the X-rays irradiated from the X-ray irradiator 6 pass through the gap and directly enter the X-ray imaging device 7. Therefore, strong X-rays are detected in the X-ray detection element corresponding to the range where the X-rays that passed through the gap in the pressure vessel T2 are incident. On the other hand, X-rays that are incident on the part of the pressure vessel T1 where no gap is formed are attenuated by the side wall of the pressure vessel T1. Therefore, weak X-rays are detected in the X-ray detection element corresponding to the range where the X-rays that have passed through the side wall of the pressure vessel T1 are incident. Therefore, based on the X-ray images of the side wall of the pressure vessel T1 taken by the X-ray irradiator 6 and the X-ray imaging device 7, it is possible to actually measure the degree to which gaps are formed in the side wall of the pressure vessel T1 (i.e., the porosity). As the deterioration of the pressure vessel T1 progresses, the porosity of the side wall increases. In other words, the porosity of the side wall of the pressure vessel T1 is a deterioration index that indicates the deterioration state of the side wall of the pressure vessel T1. Based on the X-ray images taken by the X-ray irradiator 6 and the X-ray imaging device 7, the porosity, which indicates the deterioration state of the pressure vessel T1, can be actually measured.

[0014] The X-ray irradiator 6 and the X-ray imaging device 7 move in the longitudinal direction of the pressure vessel T1 (i.e., the vertical direction on the paper surface of FIG. 1). As a result, the X-ray irradiator 6 and the X-ray imaging device 7 take an X-ray image of the entire pressure vessel T1. The X-ray imaging device 7 transmits the taken X-ray image to the control device 20.

[0015] The AE sensor 8 is a sensor that performs AE measurement of the pressure vessel T1. The AE sensor 8 is arranged so as to be in contact with the side wall of the pressure vessel T1. When the pressure vessel T1 is filled with high-pressure gas or when high-pressure gas is released from the pressure vessel T1, an elastic wave is generated in the pressure vessel T1. In other words, when a filling cycle of the pressure vessel T1 is performed, an elastic wave is generated in the pressure vessel T1. The AE sensor 8 detects the elastic wave through the side wall of the pressure vessel T1. The AE sensor 8 transmits an AE signal, which is an electric signal obtained by converting the detected elastic wave, to the control device 20. The elastic wave generated by the pressure vessel T1 during the filling cycle changes depending on the deterioration state of the pressure vessel T1. In other words, the elastic wave generated by the pressure vessel T1 is a deterioration index that indicates the deterioration state of the pressure vessel T1. The AE sensor 8 actually measures the AE signal that indicates the deterioration state of the pressure vessel T1.

[0016] The control device 20 is a computer equipped with a CPU. The control device 20 is communicably connected to the learning device 22. The control device 20 controls each device of the evaluation apparatus 10 to perform various inspections of the pressure vessel T1. The control device 20 uses an X-ray irradiation device 6 and an X-ray imaging device 7 to perform an inspection to actually measure the porosity of the cross section of the side wall of the pressure vessel T1. The control device 20 uses an AE sensor 8 to perform an inspection to actually measure the AE signal during the filling cycle of the pressure vessel T1. These inspections are performed without destroying the pressure vessel T1. In other words, these inspections are non-destructive inspections.

[0017] Furthermore, the control device 20 controls the compressor 2 and the valves 12, 14, and 16 to perform a destructive inspection of the pressure vessel T1. Specifically, the control device 20 operates the compressor 2 with the valve 16 closed and the valves 12 and 14 open. As a result, the pressure vessel T1 is filled with high-pressure gas, and the internal pressure of the pressure vessel T1 gradually increases. When the pressure vessel T1 cannot withstand the increase in internal pressure and is destroyed, the AE signal changes instantaneously. In particular, the amplitude of the AE signal changes instantaneously and greatly. The control device 20 acquires the filling pressure at the time when the amplitude of the AE signal changes instantaneously from the pressure sensor 4. The filling pressure can be estimated to be the breakdown filling pressure at the time when the pressure vessel T1 is broken. It is estimated that the lower the breakdown filling pressure of the pressure vessel T1 is, the more the deterioration of the pressure vessel T1 has progressed. In other words, it is estimated that the lower the breakdown filling pressure of the pressure vessel T1 is, the shorter the remaining life of the pressure vessel T1 is. On the other hand, the higher the burst filling pressure of the pressure vessel T1, the longer the remaining life of the pressure vessel T1 is estimated to be. In other words, the burst filling pressure is a remaining life index showing the remaining life of the pressure vessel T1. In this way, the control device 20 actually measures the burst filling pressure by destructive testing. Furthermore, the control device 20 stores a table that associates the burst filling pressure with the remaining number of fillings. This allows the control device 20 to calculate the remaining number of fillings corresponding to the burst filling pressure based on the actually measured burst filling pressure.

[0018] The learning device 22 is a computer for implementing machine learning. The learning device 22 stores a computational model 24 based on machine learning. The computational model 24 is a model for estimating the fracture filling pressure of a pressure vessel. The computational model 24 is created and updated based on the results of inspections performed by the evaluation device 10 for multiple pressure vessels. That is, the computational model 24 is created using deterioration indices (e.g., porosity, AE signal) for multiple pressure vessels and remaining life indices (e.g., fracture filling pressure) for multiple pressure vessels. The computational model 24 is a model for calculating a remaining life index, which is an output value, from a deterioration index, which is an input value, for multiple pressure vessels. In a modified example, the learning device 22 may be located at a different location from the evaluation device 10. In that case, the control device 20 and the learning device 22 may be connected via the Internet.

[0019] The pressure vessel to be evaluated in this embodiment is, for example, a high-pressure tank for storing fuel gas (e.g., hydrogen gas) to be installed in a fuel-powered vehicle. The pressure vessel has a resin liner and a reinforcing layer covering the outer surface of the liner. The reinforcing layer is made of carbon fiber reinforced plastic. The remaining life of a pressure vessel T1 having such a configuration varies depending on the usage environment of the pressure vessel T1, individual differences, etc. For this reason, it is likely to be more difficult to evaluate the remaining life of a pressure vessel T1 having such a configuration compared to pressure vessels having other configurations, such as those made of metal.

[0020] 2 and 3, the process executed by the control device 20 to create the computational model 24 using the learning device 22 will be described. FIG. 2 shows a flow diagram of the process executed by the control device 20. The control device 20 executes the process of FIG. 2 for a plurality of pressure vessels. The process of FIG. 2 is executed for a plurality of pressure vessels randomly sampled at the time of manufacturing the pressure vessels. The process of FIG. 2 is also executed for the pressure vessels installed in the fuel vehicle when the fuel vehicle equipped with the pressure vessel is collected from the market after the fuel vehicle has been driven.

[0021] In S2, the control device 20 acquires pressure vessel information. Here, as shown in FIG. 3, the pressure vessel information includes manufacturing information PI of the pressure vessel and travel information DI of the fuel vehicle in which the pressure vessel was installed. The manufacturing information PI includes process conditions and shape data of the pressure vessel. The travel information DI includes the travel distance of the fuel vehicle in which the pressure vessel was installed and the number of times the pressure vessel was filled and discharged with high pressure gas. In this embodiment, when the process of FIG. 2 is performed on the pressure vessel at the time of manufacture, an operator inputs the manufacturing information PI to the evaluation device 10. As a result, the control device 20 of the evaluation device 10 acquires the manufacturing information PI. On the other hand, when the process of FIG. 2 is performed on a pressure vessel collected from the market, the control device 20 acquires each piece of information PI, DI from a control device (not shown) of the fuel vehicle in which the pressure vessel was installed. As shown in FIG. 3, the pressure vessel information of the pressure vessels T1 to T3 at the time of manufacture does not include the travel information DI. Note that, in a modified example, even when the process of FIG. 2 is performed on a pressure vessel collected from the market, each piece of information PI, DI may be input to the evaluation device 10 by an operator.

[0022] In S4, the control device 20 causes the X-ray irradiator 6 and the X-ray image capturing device 7 (see FIG. 1) to capture an X-ray image before the fill cycle, and acquires the image.

[0023] Next, in S6, the control device 20 calculates the porosity of the side wall of the pressure vessel before the filling cycle from the X-ray image acquired in S4. In a modified example, an operator may calculate the porosity based on the X-ray image acquired in S4 and input the porosity to the evaluation device 10.

[0024] At S8, the controller 20 controls the compressor 2 and valves 12, 14, 16 to begin the fill cycle of the pressure vessel.

[0025] When the filling cycle is started in S8, in S10, the control device 20 acquires the AE signal from the AE sensor 8. The control device 20 acquires the AE signal from the AE sensor 8 while the filling cycle is being performed.

[0026] In S12, the control device 20 ends the filling cycle when the number of times the high-pressure gas has been filled into and discharged from the pressure container reaches a predetermined number. Here, the predetermined number is, for example, tens of thousands of times. In this embodiment, the control device 20 sets the predetermined number of times in the filling cycle for the pressure container at the time of manufacture to be the same as the predetermined number of times in the filling cycle for the pressure container collected from the market. In a modified example, the predetermined number of times in the filling cycle for the pressure container at the time of manufacture may be different from the predetermined number of times in the filling cycle for the pressure container collected from the market.

[0027] In S14, the control device 20 calculates the amount of change in the AE signal while the filling cycle is being performed. For example, the control device 20 calculates the amount of change in amplitude from the initial value and the final value of the amplitude of the AE signal in the filling cycle. This allows the transition of the AE signal in the filling cycle to be used to estimate the burst filling pressure. In a modified example, the control device 20 may calculate the amount of change in frequency instead of the amount of change in the amplitude of the AE signal. In a further modified example, the AE signal itself during the filling cycle may be acquired. In that case, the process of S14 can be omitted.

[0028] In S16, the control device 20 causes the X-ray irradiator 6 and the X-ray image capturing device 7 to capture an X-ray image after the filling cycle, and acquires the image.

[0029] In S18, the controller 20 calculates the porosity of the side wall of the pressure vessel after the filling cycle from the acquired X-ray images.

[0030] In S20, the control device 20 controls the compressor 2 and the valves 12, 14, and 16 to start the destructive test.

[0031] In S22, the control device 20 acquires from the pressure sensor 4 the burst filling pressure of the pressure vessel.

[0032] In S24, the control device 20 inputs to the learning device 22 the pressure vessel information acquired in S2, the void fraction before the filling cycle calculated in S6, the amplitude change in the AE signal calculated in S14, and the void fraction after filling calculated in S18.

[0033] The control device 20 executes the process of Fig. 2 for a plurality of pressure vessels. As a result, as shown in Fig. 3, the manufacturing information PI, the travel information DI, the void fraction before the filling cycle VB, the void fraction after the filling cycle VA, the amplitude change amount AE, and the burst filling pressure BP for a plurality of pressure vessels T1 to T6 are input to the learning device 22 in association with one another.

[0034] The learning device 22 creates a calculation model 24 from the input manufacturing information PI, travel information DI, porosity before the filling cycle VB, porosity after the filling cycle VA, amplitude change amount AE, and destructive filling pressure BP by machine learning. The calculation model 24 calculates an output value (i.e., destructive filling pressure BP) from the input values ​​(i.e., manufacturing information PI, travel information DI, porosity before the filling cycle VB, porosity after the filling cycle VA, and amplitude change amount AE).

[0035] In this embodiment, in addition to pressure vessels in the manufacturing stage, the process of FIG. 2 is also performed on pressure vessels collected from the market, and the information is input to the learning device 22. Therefore, information on a larger number of pressure vessels can be input to the learning device 22. Furthermore, the travel information DI of pressure vessels collected from the market can be input to the learning device 22. As a result, the computational model 24 of the learning device 22 can more accurately estimate the burst filling pressure of the pressure vessel.

[0036] A method for estimating the burst filling pressure of a pressure vessel to be evaluated will be described with reference to Fig. 4. Fig. 4 shows a flow diagram of the process executed by the control device 20. In this embodiment, for example, the process of Fig. 4 is executed for a pressure vessel collected from the market in order to estimate the burst filling pressure of the pressure vessel.

[0037] The processes of S32 to S48 are similar to the processes of S2 to S18 in Fig. 2. However, in the process of Fig. 4, the number of times of filling and discharging the high-pressure gas in the filling cycle started at S38 is less than the number of times of filling and discharging the high-pressure gas in the filling cycle started at S8 in Fig. 2. For example, in the filling cycle of Fig. 4, the filling and discharging of the high-pressure gas is performed several tens of times. In this way, by performing a fewer number of filling cycles in the process of Fig. 4 than in the filling cycle of Fig. 2, it is possible to suppress the progress of deterioration of the pressure vessel to be evaluated caused by performing the filling cycle.

[0038] In S50, the control device 20 inputs the pressure vessel information acquired in S32 (i.e., the manufacturing information PI and the travel information DI), the void fraction before the filling cycle VB calculated in S36, the amplitude change amount EA of the AE signal calculated in S44, and the void fraction after the filling cycle VA calculated in S48 to the learning device 22. The learning device 22 uses the computation model 24 from this input information to estimate the burst filling pressure of the pressure vessel to be evaluated.

[0039] In S52, the control device 20 obtains from the learning device 22 the estimated burst fill pressure.

[0040] In S54, the control device 20 calculates the remaining number of refills of the pressure vessel being evaluated based on the acquired burst refill pressure, thereby estimating the remaining life of the pressure vessel being evaluated at the current point in time.

[0041] As described above, in the remaining life assessment method of the present embodiment, the learning device 22 creates a computation model 24 by machine learning that estimates the fracture filling pressure actually measured by performing a destructive inspection from the porosity actually measured by performing a non-destructive inspection and the amount of change in amplitude of the AE signal. Furthermore, in this remaining life assessment method, the fracture filling pressure of the pressure vessel to be evaluated is estimated using the created computation model 24. This makes it possible to accurately estimate the fracture filling pressure of the pressure vessel to be evaluated from the porosity and amount of change in amplitude that can be actually measured by performing a non-destructive inspection, without actually destroying the pressure vessel to be evaluated.

[0042] Although specific examples of the technology disclosed in this specification have been described in detail above, these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and variations of the specific examples exemplified above. Modifications of the above embodiments are listed below.

[0043] (Modification 1) The evaluation device 10 does not need to include the learning device 22. In that case, the control device 20 may create the computation model 24 by statistical analysis using each piece of information shown in FIG.

[0044] (Variation 2) In the process of Fig. 2, the control device 20 may acquire X-ray images from the X-ray imaging device 7 during the filling cycle in addition to before and after the filling cycle. By calculating the porosity from the X-ray images during the filling cycle, the computational model 24 can more accurately estimate the disruptive filling pressure.

[0045] (Modification 3) The control device 20 does not have to acquire pressure vessel information. In that case, for example, the process of S2 in Fig. 2 and the process of S32 in Fig. 4 can be omitted.

[0046] (Modification 4) In this embodiment, the controller 20 calculates the remaining number of fillings from the burst filling pressure. However, in this modification, instead, the controller 20 may calculate the remaining years of use from the burst filling pressure.

[0047] The technical elements described in this specification or drawings have technical utility either alone or in various combinations, and are not limited to the combinations described in the claims at the time of filing. In addition, the technologies illustrated in this specification or drawings can achieve multiple objectives simultaneously, and achieving one of these objectives is itself technically useful. [Explanation of symbols]

[0048] 2: Compressor 4: Pressure sensor 6:X-ray irradiation device 7: X-ray imaging device 8:AE sensor 10: Evaluation device 12, 14, 16: Valve 20: Control device 22: Learning device 24: Calculation model AE: AE change amount BP: Burst filling pressure DI: Driving information PI: Manufacturing information T1~T6: Pressure vessels VA: void volume after filling cycle VB: Void ratio before filling cycle

Claims

[Claim 1] 1. A method for assessing remaining life of a pressure vessel, comprising: A step of measuring two or more deterioration indicators indicating a deterioration state of each pressure vessel by performing non-destructive testing on the plurality of pressure vessels; a step of measuring a remaining life index indicating a remaining life of each pressure vessel by carrying out a destructive test on each pressure vessel after the non-destructive test; A step of creating a calculation model for estimating the remaining life index from the two or more deterioration indexes by statistical analysis or machine learning using the two or more deterioration indexes and the remaining life index actually measured for the plurality of pressure vessels; A step of actually measuring the two or more deterioration indicators for a pressure vessel to be evaluated by performing a non-destructive inspection of the pressure vessel; calculating the remaining life index of the pressure vessel to be evaluated from the two or more deterioration indexes actually measured for the pressure vessel using the calculation model; Equipped with The non-destructive testing includes at least a first non-destructive testing using X-ray photography and a second non-destructive testing using acoustic emission measurement with a filling cycle; method.

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