JP7658319B2 - Methods for assessing remaining life of pressure vessels - Google Patents

Description

The technology disclosed in this specification relates to a method for evaluating the remaining life of a pressure vessel.

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.

JP 2011-163489 A

The deterioration of a pressure vessel varies depending on the environment in which the pressure vessel is used. For example, if the 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 has not reached a predetermined value, the pressure vessel may have deteriorated beyond the deterioration equivalent to the use of its useful life. For example, if the 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 has reached a predetermined value, the pressure vessel may not have deteriorated to the extent equivalent 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 conventional technology that evaluates the remaining life of a pressure vessel based on the number of times it is filled.

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.

In the above-mentioned method, a computation model is created by statistical analysis or machine learning using two or more degradation indices indicating the deterioration state of the pressure vessel measured by performing non-destructive testing and a life expectancy index indicating the life expectancy of the pressure vessel measured by performing destructive testing, to estimate a life expectancy 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 involving a filling cycle. Therefore, the computation model created by the method disclosed in this specification can estimate the life expectancy index of the pressure vessel using information measured using X-ray photography and information using AE measurement on the pressure vessel to be evaluated. With this configuration, the life expectancy of the pressure vessel to be evaluated can be more accurately evaluated based on each life expectancy index measured by destructive testing of multiple pressure vessels, without actually destroying the pressure vessel to be evaluated.

Details and further improvements of the technology disclosed in this specification are explained in the "Description of Embodiments" below.

1 shows a schematic diagram of an evaluation device 10 for implementing a life expectancy evaluation method according to an embodiment of the present invention. A flow diagram of the process executed by the control device 20 to create the computational model 24 is shown. An example of data input to the computational model 24 of the learning device 22 is shown. 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.

Figure 1 shows a schematic diagram of the evaluation device 10. The evaluation device 10 is a device that performs a remaining life evaluation of a pressure vessel. The evaluation device 10 is also a device that performs various inspections on multiple 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.

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 power supply to 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 is increased, 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.

The pressure sensor 4 detects the pressure in the pipe connecting the compressor 2 and the pressure vessel T1. In other words, the pressure sensor 4 measures the actual internal pressure of the pressure vessel T1. The pressure sensor 4 transmits the detected pressure value to the control device 20.

The X-ray irradiation device 6 irradiates X-rays toward the pressure vessel T1. The X-ray imaging device 7 faces the X-ray irradiation device 6 via the pressure vessel T1. The X-ray imaging device 7 has a large number of X-ray detection elements. X-rays irradiated from the X-ray irradiation device 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.

For example, when a gap is formed in the side wall of the pressure vessel T1, the X-rays irradiated from the X-ray irradiation device 6 pass through the gap and enter the X-ray imaging device 7 as is. 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 enter. On the other hand, X-rays that enter 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 enter. Therefore, based on the X-ray images of the side wall of the pressure vessel T1 taken by the X-ray irradiation device 6 and the X-ray imaging device 7, it is possible to actually measure the extent 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 from the X-ray irradiation device 6 and the X-ray imaging device 7, the porosity, which indicates the deterioration state of the pressure vessel T1, can be measured.

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.

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, elastic waves are generated in the pressure vessel T1. In other words, when a filling cycle of the pressure vessel T1 is performed, elastic waves are generated in the pressure vessel T1. The AE sensor 8 detects the elastic waves through the side wall of the pressure vessel T1. The AE sensor 8 transmits an AE signal, which is an electrical signal obtained by converting the detected elastic waves, to the control device 20. The elastic waves generated by the pressure vessel T1 during the filling cycle change depending on the deterioration state of the pressure vessel T1. In other words, the elastic waves generated by the pressure vessel T1 are 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.

The control device 20 is a computer equipped with a CPU. The control device 20 is communicatively connected to the learning device 22. The control device 20 controls each device of the evaluation device 10 to perform various inspections of the pressure vessel T1. The control device 20 uses the X-ray irradiation device 6 and the 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 the 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.

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 from the pressure sensor 4 at the time when the amplitude of the AE signal changes instantaneously. This filling pressure can be estimated to be the destruction filling pressure at the time when the pressure vessel T1 is destroyed. The lower the destruction filling pressure of the pressure vessel T1, the more the deterioration of the pressure vessel T1 is estimated to have progressed. In other words, the lower the destruction filling pressure of the pressure vessel T1, the shorter the remaining life of the pressure vessel T1 is estimated to be. 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 that indicates 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.

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 that calculates the remaining life index, which is an output value, from the 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.

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 easier to evaluate the remaining life of a pressure vessel T1 having such a configuration than pressure vessels having other configurations, such as those made of metal.

The process executed by the control device 20 to create the computational model 24 using the learning device 22 will be described with reference to Figures 2 and 3. Figure 2 shows a flow diagram of the process executed by the control device 20. The control device 20 executes the process of Figure 2 for multiple pressure vessels. The process of Figure 2 is executed for multiple pressure vessels randomly selected at the time of manufacturing the pressure vessels. The process of Figure 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.

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 the 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 high-pressure gas in the pressure vessel was filled and released. In this embodiment, when the process of FIG. 2 is performed on the pressure vessel at the time of manufacture, the worker 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 and DI from the 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 travel information DI. In a modified example, even when the process of FIG. 2 is performed on a pressure vessel that has been recalled from the market, each piece of information PI and DI may be input to the evaluation device 10 by an operator.

In S4, the control device 20 causes the X-ray irradiation device 6 and the X-ray image capture device 7 (see FIG. 1) to capture an X-ray image before the filling cycle, and acquires the image.

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.

In S8, the control device 20 controls the compressor 2 and valves 12, 14, and 16 to start the pressure vessel filling cycle.

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

In S12, the control device 20 ends the filling cycle when the number of times the high-pressure gas has been filled and released into the pressure vessel has reached 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 vessel at the time of manufacture to be the same as the predetermined number of times in the filling cycle for the pressure vessel that has been collected from the market. In a modified example, the predetermined number of times in the filling cycle for the pressure vessel at the time of manufacture may be different from the predetermined number of times in the filling cycle for the pressure vessel that has been collected from the market.

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 and final values 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 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 processing of S14 can be omitted.

In S16, the control device 20 causes the X-ray irradiation device 6 and the X-ray image capture device 7 to capture an X-ray image after the filling cycle, and acquires the image.

In S18, the control device 20 calculates the porosity of the side wall of the pressure vessel after the filling cycle from the acquired X-ray image.

In S20, the control device 20 controls the compressor 2 and the valves 12, 14, and 16 to start the destructive testing.

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

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

The control device 20 executes the process of FIG. 2 for multiple pressure vessels. As a result, as shown in FIG. 3, the manufacturing information PI, travel information DI, void ratio before the filling cycle VB, void ratio after the filling cycle VA, amplitude change amount AE, and burst filling pressure BP for multiple pressure vessels T1 to T6 are input to the learning device 22 in association with each other.

The learning device 22 uses machine learning to create a calculation model 24 from the input manufacturing information PI, driving information DI, porosity before the filling cycle VB, porosity after the filling cycle VA, amplitude change amount AE, and destructive filling pressure BP. The calculation model 24 calculates an output value (i.e., destructive filling pressure BP) from the input values (i.e., manufacturing information PI, driving information DI, porosity before the filling cycle VB, porosity after the filling cycle VA, and amplitude change amount AE).

In this embodiment, in addition to pressure vessels in the manufacturing stage, the process of FIG. 2 is also performed on pressure vessels that have been collected from the market, and the results are input to the learning device 22. This allows information on a larger number of pressure vessels to be input to the learning device 22. Furthermore, the travel information DI of pressure vessels that have been 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.

With reference to Figure 4, a method for estimating the burst filling pressure of a pressure vessel to be evaluated will be described. Figure 4 shows a flow diagram of the process executed by the control device 20. In this embodiment, for example, the process of Figure 4 is executed for a pressure vessel that has been collected from the market in order to estimate the burst filling pressure of the pressure vessel.

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 high-pressure gas is filled and released in the filling cycle that starts at S38 is less than the number of times high-pressure gas is filled and released in the filling cycle that starts at S8 in FIG. 2. For example, in the filling cycle of FIG. 4, the filling and releasing of high-pressure gas is performed several tens of times. In this way, by performing fewer filling cycles in the process of FIG. 4 than in the filling cycle of FIG. 2, the progression of deterioration of the pressure vessel being evaluated due to the implementation of the filling cycle can be suppressed.

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

In S52, the control device 20 obtains the estimated burst filling pressure from the learning device 22.

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. This allows the remaining life of the pressure vessel being evaluated at the current time to be estimated.

In this way, in the remaining life assessment method of this embodiment, the learning device 22 uses machine learning to create a calculation model 24 that estimates the burst filling pressure measured by performing a destructive test from the porosity measured by performing a non-destructive test and the amount of change in amplitude of the AE signal. Furthermore, in this remaining life assessment method, the created calculation model 24 is used to estimate the burst filling pressure of the pressure vessel to be evaluated. This makes it possible to accurately estimate the burst filling pressure of the pressure vessel to be evaluated from the porosity and amount of change in amplitude that can be measured by performing a non-destructive test, without actually destroying the pressure vessel to be evaluated.

Specific examples of the technology disclosed in this specification have been described in detail above, but 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 given above. Modifications of the above examples are listed below.

(Variation 1) The evaluation device 10 may not include the learning device 22. In that case, the control device 20 may create the computation model 24 by statistical analysis using the information shown in FIG. 3.

(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 burst filling pressure.

(Variation 3) The control device 20 does not need 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.

(Variation 4) In this embodiment, the control device 20 calculates the remaining number of fills from the burst fill pressure, but in this variation, instead, the control device 20 may calculate the remaining years of use from the burst fill pressure.

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 those objectives is itself technically useful.

2: Compressor 4: Pressure sensor 6: X-ray irradiation device 7: X-ray image capture 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: Travel information PI: Manufacturing information T1 to T6: Pressure vessel VA: Void fraction after filling cycle VB: Void fraction before filling cycle

Claims (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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