Optical fiber cable inspection point identification method

Abstract

The purpose of the present invention is to provide an inspection point identification device and method that make it possible to perform abnormality detection on an optical fiber cable in a short amount of time using DAS.　This inspection point identification device 10 comprises a vibration measurement instrument 11 that measures the vibration at an optical fiber cable 50 by DAS and an analysis processing unit 12 that analyzes vibration data measured by the vibration measurement apparatus 11 for a prescribed amount of time. The analysis processing unit 12 is characterized by acquiring the absolute values of a statistic regarding the distance of the optical fiber cable 50 from the vibration measurement instrument 11 for each prescribed amount of time, averaging each of the absolute values for the statistic to acquire an average value for each prescribed amount of time, dividing the absolute values for the statistic by the average value for each prescribed amount of time to obtain relative values for the statistic for each prescribed amount of time, detecting a center value for every distance from the relative values for the statistic for multiple instances of the prescribed amount of time, and comparing the center values for every distance to find a singular point.

Description

Optical fiber cable inspection point identification method

[0001] The present disclosure relates to a method for detecting an abnormality when applying distributed acoustic sensing (DAS), which is one of optical fiber sensing technologies.

[0002] In recent years, attention has been drawn to the use of DAS to turn optical fibers into array sensors and detect abnormalities in equipment (see, for example, Non-Patent Document 1). For example, Non-Patent Document 2 discloses that, in order to detect abnormalities related to specific phenomena, optical fiber cables for optical fiber sensing are laid in specific sections using an installation method that is less susceptible to the influence of ambient noise, and data acquired from the specific sections are subjected to vibration analysis, statistical processing, and the like, to detect abnormalities.

[0003] Lasertec Corporation, Technical Information, "Optical Fiber Distributed Vibration Sensor (DAS)," https: / / lazoc.jp / technical / das / , retrieved July 19, 2024. Kazuhiko Fujihashi, et al., "Activities in the Area of ​​Disaster Prevention Using Optical Fiber Sensing Technology," NTT Technical Review, Vol. 5, No. 10, Oct. 2007

[0004] When using DAS to detect anomalies in a communication optical fiber cable itself, the following problems arise. First, communication optical fibers are laid in an environment that is easily exposed to various natural phenomena, such as being laid in the air, and vibration patterns vary widely due to factors such as changes in wind. For this reason, a large amount of data is required in the time axis direction just to obtain vibration characteristics that have been sufficiently statistically processed. Furthermore, communication optical fiber cables are laid over distances ranging from several kilometers to several tens of kilometers. Therefore, when attempting to detect anomalies over the entire communication optical fiber cable, a large amount of data is also required in the distance direction, compared to conventional methods that only require processing data from specific sections.

[0005] As described above, detecting an abnormality in an optical fiber cable using a DAS requires calculating a huge amount of data, which makes it difficult to detect an abnormality in a short time. Therefore, in order to solve the above problem, the present invention aims to provide an inspection point identification device and method that can detect an abnormality in an optical fiber cable in a short time using a DAS.

[0006] In order to achieve the above object, the inspection point identification device of the present invention calculates the time-wise statistics of the measured vibration to create a series of absolute statistical values, and then divides this by the average value in the distance direction to create a series of relative statistical values, which are then compared between normal times and during observation, thereby identifying the inspection point.

[0007] Specifically, the inspection point identification device of the present invention is an inspection point identification device comprising: a vibration measuring instrument that measures vibrations of an optical fiber cable that is the object of measurement using DAS (Distributed Acoustic Sensing); and an analysis processing unit that analyzes vibration data measured by the vibration measuring instrument for a predetermined time, wherein the analysis processing unit performs statistical processing on each of the vibration data to obtain absolute values ​​of statistics for each of the predetermined time periods with respect to the distance of the optical fiber cable from the vibration measuring instrument; averages each absolute value of the statistics to obtain an average value for each of the predetermined time periods; divides the absolute value of the statistics by the average value for each of the predetermined time periods to obtain relative values ​​of the statistics for each of the predetermined time periods; detects a median for each distance from among the relative values ​​of the statistics for a plurality of the predetermined time periods; compares the median value obtained at a reference time with the median value obtained at the time of observation for each distance to find a singular point; and outputs the position of the optical fiber cable that corresponds to the distance of the singular point as an inspection point.

[0008] Furthermore, the method for identifying inspection points according to the present invention is a method for identifying inspection points of an optical fiber cable by having an analysis processing unit analyze vibration data for a predetermined time period obtained by measuring the vibration of the optical fiber cable to be measured using DAS (Distributed Acoustic Sensing), wherein the analysis processing unit: performs statistical processing on each of the vibration data to obtain absolute values ​​of statistics for each of the predetermined time periods relative to the distance from the vibration measuring device of the optical fiber cable; averages each absolute value of the statistics to obtain an average value for each of the predetermined time periods; divides the absolute value of the statistics by the average value for each of the predetermined time periods to obtain relative values ​​of the statistics for each of the predetermined time periods; detects a median for each distance from among the relative values ​​of the statistics for a plurality of the predetermined time periods; compares the median value obtained at a reference time with the median value obtained at the time of observation for each distance to find a singular point; and outputs the position of the optical fiber cable corresponding to the distance of the singular point as an inspection point.

[0009] The technique of the present invention can reduce the amount of data required to calculate an index value for anomaly detection (the mean or median of a statistical series), i.e., shorten the measurement time. Therefore, the present invention can provide an inspection point identification device and method that can detect anomalies in an optical fiber cable in a short time using a DAS.

[0010] The analysis processing unit of the inspection point identification device according to the present invention preferably includes a filter that removes a desired frequency from the vibration of the optical fiber cable. The filter increases the difference between the series of statistical relative values ​​during normal operation and during observation, thereby further reducing the amount of data required, i.e., further shortening the measurement time.

[0011] The present invention also provides a program for causing a computer to function as the analysis processing unit of the inspection point identification device. The inspection point identification device of the present invention can be realized by a computer and a program, and the program can be recorded on a recording medium or provided via a network.

[0012] The above inventions can be combined as much as possible.

[0013] The present invention can provide an inspection point specifying device and method that can detect abnormalities in optical fiber cables in a short time using a DAS.

[0014] FIG. 1 is a diagram illustrating the configuration of an inspection point identifying device according to the present invention. FIG. 1 is a diagram illustrating the configuration of an inspection point identifying device according to the present invention. FIG. 2 is a diagram illustrating an inspection point identifying method according to the present invention. FIG. 3 is an example of a file created by dividing measured vibration data. FIG. 4 is an example of a file created by dividing measured vibration data. FIG. 5 is a diagram illustrating processing performed by an analysis processing unit of an inspection point identifying device according to the present invention. FIG. 6 is a diagram illustrating processing performed by an analysis processing unit of an inspection point identifying device according to the present invention. FIG. 7 is a diagram illustrating processing performed by an analysis processing unit of an inspection point identifying device according to the present invention. FIG. 8 is a diagram illustrating processing performed by an analysis processing unit of an inspection point identifying device according to the present invention. FIG. 9 is a diagram illustrating processing performed by an analysis processing unit of an inspection point identifying device according to the present invention. FIG. 10 is a diagram illustrating processing performed by an analysis processing unit of an inspection point identifying device according to the present invention.

[0015] The following description of the preferred embodiments of the present invention will be given with reference to the accompanying drawings. The preferred embodiments described below are examples of the present invention, and the present invention is not limited to the preferred embodiments. In this specification and the drawings, components having the same reference numerals are intended to represent the same components.

[0016] 1 and 2 are diagrams illustrating the configuration of an inspection point identifying device 10 according to this embodiment. The inspection point identifying device 10 includes a vibration measuring device 11 that measures vibrations of an optical fiber cable 50, which is a measurement target, by distributed acoustic sensing (DAS), and an analysis processing unit 12 that analyzes vibration data measured by the vibration measuring device 11 for a predetermined period of time. The analysis processing unit 12 is characterized by: performing statistical processing on each of the vibration data to obtain the absolute value of the statistical quantity for the distance from the vibration measuring device of the optical fiber cable 50 for each of the specified times; averaging each of the absolute values ​​of the statistical quantity to obtain an average value for each of the specified times; dividing the absolute value of the statistical quantity by the average value for each of the specified times to obtain a relative value of the statistical quantity for each of the specified times; detecting a median for each of the relative values ​​of the statistical quantities for a plurality of the specified times; comparing the median value obtained at the reference time with the median value obtained at the time of observation for each distance to find a singular point; and outputting the position of the optical fiber cable 50 corresponding to the distance of the singular point as an inspection point.

[0017] The optical fiber cable 50 includes an underground optical fiber cable 51 buried underground, an aerial optical fiber cable 52 suspended in the air from utility poles, and a submarine optical fiber cable 53 placed on the seabed in an ocean section. The optical fiber cable 50 contains multiple communication optical fibers. The vibration measuring instrument 11 inputs test pulse light into one or multiple communication optical fibers included in the optical fiber cable 50 and receives backscattered light generated in the optical fibers. Alternatively, if the optical fiber cable 50 includes an optical fiber for vibration inspection, the vibration measuring instrument 11 may input test pulse light into the optical fiber for vibration inspection and receive the backscattered light.

[0018] In order to receive vibrations at any point, the vibration measuring device 11 measures vibrations occurring in an optical fiber by irradiating a test pulse light onto the optical fiber installed above the road, on the road surface, under the road, or in the ocean, and receiving the backscattered light. The vibration measuring device 11 can then output the vibrations received by the optical fiber as a distribution in the longitudinal direction of the optical fiber.

[0019] The analysis processing unit 12 calculates and stores vibration feature quantities for each distance (point) of the optical fiber from the vibration distribution occurring in the optical fiber in a normal state measured by the vibration measuring device 11. Furthermore, the analysis processing unit 12 calculates vibration feature quantities for each distance (point) of the optical fiber from the vibration distribution occurring in the optical fiber during monitoring measured by the vibration measuring device 11. The analysis processing unit 12 then calculates a ratio between the vibration feature quantity in the normal state and the vibration feature quantity in the monitoring state for each distance (point) of the optical fiber, and determines that an inspection is required for the optical fiber at which the ratio of the vibration feature quantities exceeds an arbitrary threshold. The analysis processing unit 12 may display the distance (position) of the optical fiber determined to require inspection on the display unit 13. Furthermore, the analysis processing unit 12 may compare the distance (position) of the optical fiber determined to require inspection with the actual positional relationship of the optical fiber cable 50, identify a point at which the optical fiber cable 50 should be inspected, and display the same on the display unit 13.

[0020] 3 is a diagram illustrating an inspection point identification method performed using the inspection point identification device 10. That is, this inspection point identification method is an inspection point identification method in which the analysis processing unit 12 analyzes vibration data (steps S2 and S4) obtained for a predetermined time period when the vibration measuring device 11 measures the vibration of the optical fiber cable 50, which is the measurement target, using the DAS, to identify the inspection point of the optical fiber cable 50. The analysis processing unit 12 then performs statistical processing on each of the vibration data to obtain the absolute value of the statistical quantity for each of the predetermined times relative to the distance from the vibration measuring device 11 of the optical fiber cable 50 (steps S3 and S5); averages each of the absolute values ​​of the statistical quantity to obtain an average value for each of the predetermined times (steps S3 and S5); divides the absolute value of the statistical quantity by the average value for each of the predetermined times to obtain a relative value of the statistical quantity for each of the predetermined times (steps S3 and S5); detects a median for each of the relative values ​​of the statistical quantities for a plurality of the predetermined times (step S6); compares the median obtained at the reference time with the median obtained at the time of observation for each distance to find a singular point (step S6); and outputs the position of the optical fiber cable corresponding to the distance of the singular point as an inspection point (step S7).

[0021] Each step will be described in detail below. [Step S1] Understanding of Positional Relationships Geographical information indicating the position of the optical fiber in the longitudinal direction of the optical fiber cable 50 (the distance of the optical fiber cable 50 from the vibration measuring device 11) that is laid above a road, on the road surface, under the road, or on the seabed, is input to the analysis processing unit 12. This data is provided to the analysis processing unit 12 in advance.

[0022] [Step S2] Normal vibration measurement: When it is determined that the optical fiber cable 50 is in a normal state, the vibration occurring in the optical fiber cable 50 is measured for an arbitrary period of time by the vibration measuring instrument 11. Note that the "normal state" refers to a reference state (reference time). The vibration measuring instrument 11 outputs the measurement results as measured vibration data.

[0023] [Step S3] Calculation of Normal Vibration Characteristics. This step is performed by the analysis processing unit 12. First, the measured vibration data obtained in step S2 over the entire measurement period under normal conditions is divided into files for any section and time period where similar vibration characteristics are observed. Here, "sections where similar vibration characteristics are observed" refers to "cable sections that receive the same wind in terms of the cable installation state and surrounding structures." "Time periods where similar vibration characteristics are observed" refers to times when there is no significant deviation in the average wind speed (for example, wind speeds within ±5 m / s). This is determined based on the geographic information obtained in step S1 and the measured vibration data obtained in step S2. Alternatively, files may be created by simply dividing the data into predetermined sections (for example, every 250 m) and time periods (every 30 seconds). Figure 4 shows an example of a file created by dividing the measured vibration data. Figure 5 shows the measurement results of Figure 4 expressed in numerical values ​​(amplitude values). In this example, any 30-second period of measured vibration data is divided into 250 m intervals in the distance direction, and the file is for any one of these intervals (1850 m to 2100 m interval).

[0024] Next, for each file, the time-direction statistics of the vibration amplitude values ​​are calculated for each point on the optical fiber cable 50, and a series of absolute statistical values ​​in a normal state is created. Here, the statistics are the variance or standard deviation of the vibration amplitude values ​​in the time direction, or the cumulative power obtained by adding the absolute values ​​or squares of the vibration amplitude values ​​in the time direction. This will be explained in detail using FIG. 5. In this explanation, an example in which the statistics is cumulative power will be explained. As shown in FIG. 5, one file is made up of a matrix, and the row direction is the x values ​​included in the distance section from 1850 m to 2100 m. １ Point x Ｎ The vibration amplitude values ​​are arranged in N positions up to the point S, and the column direction is arranged in the number of vibration amplitude values ​​measured in the time direction (for 30 seconds). ｉ Then, one point x ｎ Cumulative power Px ｎ is calculated using the following formula: where i is the data number in the time direction (a natural number from 0 to T), T is the number of data in the time direction (30 seconds in this example) contained in one file, and n is the location number (a natural number from 1 to N) contained in one file.

[0025] From one file, the cumulative power of each point is calculated using formula (1) to create a list (a series of absolute statistical values) (see Figure 6). A list is created for all files. Figure 7 shows the cumulative power distribution graphed from one list.

[0026] Next, the average value of the statistics in the longitudinal direction of the optical fiber is calculated. That is, the average value AVE (Px ｎ ) is calculated (see FIG. 8). Then, the series of absolute statistics values ​​is divided by the average value to create a series of relative statistics values. That is, each cumulative power included in the list is divided by the average value of the list to calculate a list of relative cumulative power values. Figure 9 is a distribution diagram of relative cumulative power values ​​created based on the list of relative cumulative power values.

[0027] Furthermore, the average or median of the statistical relative values ​​of multiple files is calculated for each distance of the optical fiber cable 50, and saved as a vibration characteristic (normal vibration characteristic) when the optical fiber cable 50 is in a normal state. That is, the median (or average) of the relative values ​​of the accumulated power at each point is calculated from a list of multiple relative values ​​of the accumulated power measured at different times, and saved. Figure 10 is a distribution diagram of the calculated medians of the relative values ​​of the accumulated power.

[0028] [Step S4] While the vibration monitoring optical fiber cable 50 is being monitored, the vibration occurring in the optical fiber cable 50 is measured for an arbitrary period of time by the vibration measuring instrument 11. The vibration measuring instrument 11 outputs the measurement results as measured vibration data.

[0029] [Step S5] Calculation of monitoring vibration characteristics This step is performed by the analysis processing unit 12. The processing performed in this step is the same as the processing described in step S03, except that the processing target is changed to vibration data obtained during the monitoring period. First, the measured vibration data obtained during the monitoring period in step S4 is divided into files. The unit of division into files is the same as the unit of division into files of the measured vibration data in a normal state performed in step S3. In the following explanation, as in step S3, any 30-second worth of measured vibration data is divided into 250-m intervals in the distance direction, and the explanation will be given using a file for any one of these intervals (1850 m to 2100 m interval).

[0030] Similarly, in this step, a series of absolute statistical values ​​(a list of cumulative power for each point) for the monitoring period is created, and each cumulative power is divided by its average value to calculate a series of relative statistical values ​​(a list of relative cumulative power values). Then, the average or median of the relative statistical values ​​for multiple files is calculated for each distance of the optical fiber cable 50, and saved as a vibration feature (monitored vibration feature) for the period during which the optical fiber cable 50 is monitored. In other words, the median (or average) of the relative cumulative power values ​​for each point is calculated and saved from a list of multiple relative cumulative power values ​​measured at different times. Figure 11 is a distribution diagram of the calculated median relative cumulative power values.

[0031] [Step S6] Threshold Overrun Determination This step is performed by the analysis processing unit 12. In this step, for each of the arbitrary sections, the monitored vibration feature is divided by the normal vibration feature for each point on the optical fiber cable 50 to calculate a ratio of the vibration statistics relative values. An abnormality is determined to have occurred at a location where this ratio deviates from an arbitrary numerical determination range (e.g., 0.3 to 3), and the distance on the optical fiber where the abnormality was determined to have occurred is detected (see FIG. 12). The abnormality locations detected for each section are sorted by distance, and the distance on the optical fiber of the abnormality location throughout the optical fiber cable 50 is recorded. The numerical determination range is set to 0.5 to 2, for example, when an abnormality is determined to have occurred when the monitored vibration feature differs by more than two times compared to the normal vibration feature.

[0032] [Step S7] Estimating Locations Requiring Inspection This step is performed by the analysis processing unit 12. In this step, the location of the locations requiring inspection of the optical fiber cable 50 is estimated by comparing the distance along the optical fiber from the location of the abnormality recorded in step S6 with the geographic information input in step S1 (see FIG. 12).

[0033] Finally, the analysis processing unit 12 causes the display unit 13 to display the inspection-requiring locations estimated in step S7.

[0034] 13A and 13B are diagrams illustrating the effects of the present invention. Fig. 13A shows a comparative example in which vibration features are calculated using absolute statistical values, and Fig. 13B shows an example in which vibration features are calculated using relative statistical values. Focus is placed on three points (A, B, and C) on the optical fiber. It is assumed that there are no abnormalities at points A and C, but that there is an abnormality at point B.

[0035] In the comparative example, when the number of files was around 500, the anomaly index was at the same level for all points. This means that the amount of data was insufficient for statistical processing. Also, in the comparative example, the anomaly index increased when the number of files was around 1,500. This means that when oscillations such as outliers (noise or measurement errors) occurred, the anomaly index was dragged along by those values, slowing down convergence (taking longer to measure).

[0036] On the other hand, in the example, the values ​​converge when the number of files is about 250. This means that the convergence is fast (measurement time can be shortened). Furthermore, in the example, the phenomenon of being dragged down by the outlier value around 1500 files, which was seen in the comparative example, does not occur.

[0037] The inspection point identification device 10 can achieve the following effects by having the analysis processing unit 12 create a list of absolute statistical values ​​(such as cumulative power) for each point from the vibration data of the optical fiber, and then create a list of relative statistical values ​​by dividing each absolute statistical value by its average value: [Effect] The number of files (= data volume and measurement time) required to calculate a value that serves as an index for abnormality determination (average or median of the statistical series) can be reduced, enabling abnormal points (inspection points) to be detected more quickly.

[0038] (Embodiment 2) The configuration of an inspection point identifying device of this embodiment is the same as the configuration of the inspection point identifying device 10 described in Figures 1 and 2. The inspection point identifying device of this embodiment performs an arbitrary filter process on the inspection point identifying device 10 described in Figures 1 and 2 before calculating the absolute values ​​of statistics in steps S3 and S5.

[0039] The analysis processing unit 12 is equipped with a filter that removes desired frequencies from a file of measured vibration data such as that shown in FIG. 4. Processing using this filter makes it possible to reveal vibration characteristics due to specific abnormalities. FIG. 14 is a diagram illustrating the effect of filter processing. FIG. 14(A) shows measured vibration data obtained by subjecting the measured vibration data shown in FIG. 4 to high-pass filtering with a cutoff frequency of 0.5 Hz. FIG. 14(B) shows the measured vibration data converted into a series of absolute statistical values.

[0040] On the other hand, Fig. 14(C) shows measured vibration data obtained by subjecting the measured vibration data shown in Fig. 4 to band-pass filtering with a lower cutoff frequency of 20 Hz and an upper cutoff frequency of 30 Hz. Fig. 14(D) shows the measured vibration data converted into a series of absolute statistical values.

[0041] 14, the difference in the statistical series values ​​between the locations where an abnormality occurs (points indicated by arrows) and the locations where an abnormality does not occur becomes large. This means that it is possible to calculate the vibration features in step S3 or S5 even if the number of files is small.

[0042] (Embodiment 3) The analysis processing unit 12 can also be realized by a computer and a program, and the program can be recorded on a recording medium or provided via a network. Figure 15 shows a block diagram of a system 100. The system 100 includes a computer 105 connected to a network 135.

[0043] Network 135 is a data communications network. Network 135 may be a private or public network and may include any or all of the following: (a) a personal area network, e.g., covering a room; (b) a local area network, e.g., covering a building; (c) a campus area network, e.g., covering a campus; (d) a metropolitan area network, e.g., covering a city; (e) a wide area network, e.g., covering an area spanning city, region, or country boundaries; or (f) the Internet. Communications are conducted over network 135 by electronic and optical signals.

[0044] The computer 105 includes a processor 110 and a memory 115 connected to the processor 110. While the computer 105 is depicted herein as a standalone device, it is not so limited and may be connected to other devices (not shown) in a distributed processing system. For example, a program implementing the present invention may run on a cloud server (e.g., AWS EC2), and the results may be received by a user on a user device 130 via a network 135.

[0045] Processor 110 is an electronic device made up of logic circuits that responds to and carries out instructions.

[0046] The memory 115 is a tangible computer-readable storage medium on which a computer program is encoded. In this regard, the memory 115 stores data and instructions, i.e., program code, that can be read and executed by the processor 110 to control its operation. The memory 115 can be implemented as a random access memory (RAM), a hard drive, a read-only memory (ROM), or a combination thereof. One component of the memory 115 is a program module 120.

[0047] The program modules 120 contain instructions for controlling the processor 110 to perform the processes described herein. Although operations are described herein as being performed by the computer 105 or a method or process or sub-process thereof, those operations are actually performed by the processor 110.

[0048] The term "module" is used herein to refer to a functional operation that may be embodied as either a stand-alone component or an integrated configuration of multiple subcomponents. Thus, program module 120 may be implemented as a single module or as multiple modules operating in cooperation with each other. Furthermore, although program module 120 is described herein as being installed in memory 115 and therefore implemented in software, it may be implemented in any of hardware (e.g., electronic circuitry), firmware, software, or a combination thereof.

[0049] While the program modules 120 are shown as already loaded into memory 115, they may also be configured to reside on storage device 140 for later loading into memory 115. Storage device 140 is a tangible, computer-readable storage medium that stores the program modules 120. Examples of storage device 140 include compact discs, magnetic tape, read-only memory, optical storage media, a memory unit consisting of a hard drive or multiple parallel hard drives, and a universal serial bus (USB) flash drive. Alternatively, storage device 140 may be random access memory or another type of electronic storage device located in a remote storage system (not shown) and connected to computer 105 via network 135.

[0050] System 100 further includes data source 150A and data source 150B, collectively referred to herein as data sources 150, that are communicatively connected to network 135. In practice, data sources 150 may include any number of data sources, i.e., one or more data sources. Data sources 150 may include unstructured data and may include social media.

[0051] The system 100 further includes a user device 130 operated by the user 101 and connected to the computer 105 via a network 135. The user device 130 includes an input device, such as a keyboard or a voice recognition subsystem, that allows the user 101 to communicate information and command selections to the processor 110. The user device 130 also includes an output device, such as a display device or a printer or a voice synthesizer. A cursor control, such as a mouse, trackball, or touch-sensitive screen, allows the user 101 to manipulate a cursor on the display device to communicate further information and command selections to the processor 110.

[0052] The processor 110 outputs the results 122 of the execution of the program modules 120 to the user device 130. Alternatively, the processor 110 can provide the output to a storage device 125, such as a database or memory, or via a network 135 to a remote device not shown.

[0053] For example, a program that performs steps S3, S5, S6, and S7 in the flowchart of FIG.

[0054] The terms "comprising" or "comprising" should be interpreted as specifying the presence of the stated features, integers, steps or components, but not excluding the presence of one or more other features, integers, steps or components or groups thereof. The terms "a" and "an" are indefinite articles and therefore do not exclude embodiments having a plurality thereof.

[0055] (Other Embodiments) The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the present invention. In short, the present invention is not limited to the above-described embodiment, and the components can be modified and embodied in the implementation stage without departing from the spirit of the present invention.

[0056] Furthermore, various inventions can be formed by appropriately combining the multiple components disclosed in the above embodiments. For example, some components may be omitted from all the components shown in the embodiments. Furthermore, components from different embodiments may be appropriately combined.

[0057] 10: Inspection point identification device 11: Vibration measuring device 12: Analysis processing unit 13: Display unit 50: Optical fiber cable 51: Underground optical fiber cable 52: Aerial optical fiber cable 53: Submarine optical fiber cable 100: System 101: User 105: Computer 110: Processor 115: Memory 120: Program module 122: Result 125: Storage device 130: User device 135: Network 140: Storage device 150: Data source

Claims

1. An inspection point identification device comprising: a vibration measuring device that measures the vibration of an optical fiber cable that is the object of measurement using DAS (Distributed Acoustic Sensing); and an analysis processing unit that analyzes vibration data measured by the vibration measuring device for a predetermined time period, wherein the analysis processing unit: performs statistical processing on each of the vibration data to obtain the absolute value of a statistical quantity for each of the predetermined time periods relative to the distance of the optical fiber cable from the vibration measuring device; averages each of the absolute values ​​of the statistical quantities to obtain an average value for each of the predetermined time periods; divides the absolute value of the statistical quantity by the average value for each of the predetermined time periods to obtain a relative value of the statistical quantity for each of the predetermined time periods; detects a median for each distance from among the relative values ​​of the statistical quantities for a plurality of the predetermined time periods; compares the median value obtained at a reference time with the median value obtained at the time of observation for each distance to find a singular point; and outputs the position of the optical fiber cable that corresponds to the distance of the singular point as an inspection point.

2. The inspection point identification device according to claim 1, characterized in that the analysis processing unit is provided with a filter that removes a desired frequency from the vibration of the optical fiber cable.

3. A method for identifying inspection points in an optical fiber cable by having an analysis processing unit analyze vibration data for a predetermined time period obtained by measuring the vibration of the optical fiber cable being measured using DAS (Distributed Acoustic Sensing), wherein the analysis processing unit: performs statistical processing on each of the vibration data to obtain absolute values ​​of statistics for each of the predetermined time periods relative to the distance from the vibration measuring device of the optical fiber cable; averages each absolute value of the statistics to obtain an average value for each of the predetermined time periods; divides the absolute value of the statistics by the average value for each of the predetermined time periods to obtain relative values ​​of the statistics for each of the predetermined time periods; detects a median for each distance from the relative values ​​of the statistics for a plurality of the predetermined time periods; compares the median value obtained at a reference time with the median value obtained at the time of observation for each distance to find a singular point; and outputs the position of the optical fiber cable corresponding to the distance to the singular point as an inspection point.

4. A program for causing a computer to function as the analysis processing unit provided in the inspection point identification device of claim 1 or 2.

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