JPH07333171A - Leak detection method and device - Google Patents
Leak detection method and deviceInfo
- Publication number
- JPH07333171A JPH07333171A JP13157994A JP13157994A JPH07333171A JP H07333171 A JPH07333171 A JP H07333171A JP 13157994 A JP13157994 A JP 13157994A JP 13157994 A JP13157994 A JP 13157994A JP H07333171 A JPH07333171 A JP H07333171A
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- Japan
- Prior art keywords
- image
- abnormality
- difference
- error
- plant
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Abstract
Description
【０００１】[0001]
【産業上の利用分野】本発明は、撮像した機器の画像情
報を用いて機器の異常に伴う流体漏洩を自動的に検出す
るための方法及び装置に係り、特に、流体漏洩の種別に
関わらず高感度で検出し、その種別を自動的に判定する
のに好適な漏洩検出方法及び装置に関する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and an apparatus for automatically detecting a fluid leakage due to an abnormality of a device by using image information of the imaged device, and in particular, regardless of the type of the fluid leakage. The present invention relates to a leak detection method and apparatus suitable for detecting with high sensitivity and automatically determining the type.
【０００２】[0002]
【従来の技術】ＩＴＶカメラによる可視画像を用いてプ
ラント内の流体漏洩を検出する装置に関する従来技術と
しては、平成元年電気学会全国大会発表予稿集Ｎo.１５
９３に記載の「プラント自動巡視点検ロボットの開発
（その２）」で述べられている時系列画像の差分処理と
いう方法がある。この方法は流体漏洩に伴う水滴や蒸気
が一定時間後にはその位置を変えることを利用し、時系
列に取り込んだ画像間の差分処理を行うことにより、異
常を検出する手法である。2. Description of the Related Art As a conventional technique relating to a device for detecting fluid leakage in a plant by using a visible image from an ITV camera, there is a proceedings No. 15 of the National Conference of the Institute of Electrical Engineers of Japan in 1989.
There is a method called difference processing of time-series images described in “Development of automatic plant inspection and inspection robot (2)” described in No. 93. This method utilizes the fact that the position of water droplets or steam associated with fluid leakage changes after a certain period of time, and performs a difference process between images captured in time series to detect anomalies.
【０００３】[0003]
【発明が解決しようとする課題】上記従来技術では、可
視カメラで撮像した時系列の画像間の差分処理を行い、
差分結果の２値画像を累積することによって異常を検出
する手法を用いている。このため、この従来技術では検
出した異常事象の種別（水漏れまたは蒸気漏れ）までは
特定することはできなかった。In the above prior art, the difference processing between time-series images captured by a visible camera is performed,
The method of detecting an abnormality by accumulating the binary images of the difference results is used. For this reason, it has not been possible to identify the type of abnormal event (water leak or steam leak) that has been detected in this conventional technique.
【０００４】本発明の目的は、プラント内の機器の異常
を監視する装置において流体漏洩に伴う異常種別の自動
判定が可能な漏洩検出方法及び装置を提供することにあ
る。An object of the present invention is to provide a leak detection method and device capable of automatically determining the type of abnormality associated with fluid leakage in a device for monitoring abnormality of equipment in a plant.
【０００５】[0005]
【課題を解決するための手段】上記目的を達成するため
に、本発明は以下の手段を用いる。異常検知の基本的手
法としては、可視カメラで撮像した時系列の画像間の差
分処理により異常検知する手法を用いる。ここで、蒸気
漏れなどの異常は状態変化の時定数の長い事象であるた
め、長い差分周期の差分処理を用いた方が感度よく検出
できる。一方、水漏れなどの異常は状態変化の時定数の
短い事象であるため、短い差分周期の差分処理を用いた
方が感度よく検出できる。このため、長い差分周期の差
分処理を用いて求めた異常検出面積と短い差分周期の差
分処理を用いて求めた異常検出面積を比較することによ
り、検出した異常事象が状態変化の時定数の長い異常
（蒸気漏れなど）であるか状態変化の時定数の短い異常
（水漏れ）であるかを判定することができる。このよう
な手法により、流体漏洩に伴う異常種別の自動判定が可
能となる。In order to achieve the above object, the present invention uses the following means. As a basic method of detecting an abnormality, a method of detecting an abnormality by difference processing between time-series images captured by a visible camera is used. Here, since an abnormality such as steam leakage is an event with a long time constant of state change, it is possible to detect with higher sensitivity by using the difference processing with a long difference period. On the other hand, since an abnormality such as water leakage is an event with a short time constant of state change, it can be detected with higher sensitivity by using the difference processing with a shorter difference period. Therefore, by comparing the anomaly detection area obtained by using the difference processing with a long difference cycle and the anomaly detection area obtained by using the difference processing with a short difference cycle, the detected abnormal event has a long time constant of state change. It is possible to determine whether it is an abnormality (such as steam leakage) or an abnormality (water leakage) with a short time constant of state change. With such a method, it is possible to automatically determine the type of abnormality associated with fluid leakage.
【０００６】[0006]
【作用】まず、可視カメラで撮像した監視対象の可視画
像を短い差分周期の分処理をすることにより、異常を２
値画像として検出し、その２値画像の面積を求める。次
に、差分周期を長い値に設定し、長い差分周期の差分処
理をすることにより、異常を２値画像として検出し、そ
の２値画像の面積を求める。さらに、この求めた２値画
像同志を比較し、異常事象の種別を判定する。異常事象
の種別の判定では、短い差分周期の差分処理から求めた
面積が大きい場合は状態変化の時定数の短い異常（水漏
れなど）であるとし、長い差分周期の差分処理から求め
た面積が大きい場合は状態変化の時定数の長い異常（蒸
気漏れなど）であるとする。このような手法により、流
体漏洩に伴う異常種別の自動判定を実施する。First, the visible image of the monitoring target imaged by the visible camera is processed for a short difference period to detect the abnormality.
The value image is detected, and the area of the binary image is obtained. Next, the difference cycle is set to a long value, and the difference processing of the long difference cycle is performed to detect the abnormality as a binary image, and the area of the binary image is obtained. Further, the obtained binary images are compared with each other to determine the type of abnormal event. In the determination of the type of abnormal event, if the area obtained from the difference processing of the short difference cycle is large, it is considered as an abnormality with a short time constant of state change (water leakage etc.), and the area obtained from the difference processing of the long difference cycle is If it is large, it means that the status change is abnormal with a long time constant (such as steam leakage). By such a method, the automatic determination of the abnormality type associated with the fluid leakage is performed.
【０００７】[0007]
【実施例】以下、本発明の実施例を図を用いて説明す
る。まず、本発明の概要を図２〜図４を用いて説明す
る。図２は蒸気漏れなどの状態変化の時定数の長い異常
を対象とした場合における本発明を用いたプラント内の
流体漏洩の検知手法を説明するための図である。図２
（ａ）はプラント内の流体漏洩の模式図である。配管８
２に接続されたフランジ８１から蒸気漏れ８３，８３′
が発生している。この監視対象を可視カメラ１で撮像し
た画像を用いて異常を検知する。図２（ｂ）は可視カメ
ラ１で撮像した監視対象の可視画像を示す。画像１１ａ
は時刻ｔの時点で撮像した監視対象の画像である。画像
１１ｂは時刻ｔ＋Δｔ1 の時点で撮像した監視対象の画
像である。蒸気漏れは状態変化の時定数の長い異常であ
るため、この場合の差分周期Δｔ1 は長くないと画像間
で状態変化のある画像が得られない。そこで、差分周期
Δｔ1 は例えば、約１〜２秒程度の数値に設定する。さ
らに画像１１ｃは画像１１ｂと画像１１ａの差分画像で
あり、蒸気漏れによる異常のみを検出できる。Embodiments of the present invention will be described below with reference to the drawings. First, the outline of the present invention will be described with reference to FIGS. FIG. 2 is a diagram for explaining a method for detecting fluid leakage in a plant using the present invention when an abnormality having a long time constant of state change such as steam leakage is targeted. Figure 2
(A) is a schematic diagram of fluid leakage in a plant. Piping 8
Steam leaks 83, 83 'from a flange 81 connected to 2
Is occurring. An abnormality is detected by using an image of the monitoring target captured by the visible camera 1. FIG. 2B shows a visible image of the monitoring target captured by the visible camera 1. Image 11a
Is an image of a monitoring target captured at time t. The image 11b is an image of the monitoring target captured at time t + Δt 1 . Since steam leakage is an abnormality with a long time constant of state change, images with a state change between images cannot be obtained unless the difference cycle Δt 1 in this case is long. Therefore, the difference cycle Δt 1 is set to a numerical value of about 1 to 2 seconds, for example. Further, the image 11c is a difference image between the image 11b and the image 11a, and it is possible to detect only the abnormality due to the steam leak.
【０００８】一方、図３は水漏れなどの状態変化の時定
数の短い異常を対象とした場合における本発明を用いた
プラント内の流体漏洩の検知手法を説明するための図で
ある。図３（ａ）はプラント内の流体漏洩の模式図であ
る。配管８２に接続されたフランジ８１から水漏れ８
４，８４′が発生している。図３（ｂ）は可視カメラ１
で撮像した監視対象の可視画像を示す。画像１１ａは時
刻ｔの時点で撮像した監視対象の画像である。画像１１
ｂは時刻ｔ＋Δｔ1 の時点で撮像した監視対象の画像で
ある。水漏れは状態変化の時定数の短い異常であるた
め、この場合の差分周期Δｔ2 は短くないと画像間で状
態変化のある画像が得られない。そこで、差分周期Δｔ
2 は例えば、数１００ｍｓ程度の数値に設定する。さら
に画像１１ｃは画像１１ｂと画像１１ａの差分画像であ
り、水漏れによる異常のみを検出できる。On the other hand, FIG. 3 is a diagram for explaining a method for detecting fluid leakage in a plant using the present invention when an abnormality having a short time constant of state change such as water leakage is targeted. FIG. 3A is a schematic diagram of fluid leakage in the plant. Water leakage 8 from the flange 81 connected to the pipe 82
4, 84 'have occurred. FIG. 3B shows a visible camera 1.
The visible image of the monitoring target imaged in FIG. The image 11a is an image of the monitoring target captured at time t. Image 11
b is an image of the monitoring target imaged at time t + Δt 1 . Since water leakage is an abnormality with a short time constant of state change, an image with a state change between images cannot be obtained unless the difference cycle Δt 2 in this case is short. Therefore, the difference cycle Δt
For example, 2 is set to a value of about several 100 ms. Further, the image 11c is a difference image between the image 11b and the image 11a, and only an abnormality due to water leakage can be detected.
【０００９】図４は蒸気漏れなどの状態変化の時定数の
長い異常を対象とした場合に本発明を適用し、流体漏洩
の異常種別を判定した例である。画像１２ａは短い差分
周期で差分処理した差分画像１１ｃを２値化して異常を
検出した結果である。差分画像１２ａ上には異常を２値
画像８３ａとして検出している。また、画像１２ｂは長
い差分周期で差分処理した差分画像１１ｃを２値化して
異常を検出した結果である。差分画像１２ｂ上には異常
を２値画像８３ｂとして検出している。ここで、この場
合の２値画像の面積を比較すると差分周期大の方が大き
いことがわかる。このように蒸気漏れなど状態変化の時
定数の長い異常の場合は差分周期大の差分処理で求めた
２値画像の面積の方が大きくなる。一方、水漏れなどの
状態変化の時定数の短い異常の場合は差分周期小の差分
処理で求めた２値画像の面積の方が大きくなる。このよ
うに差分周期を変えた差分処理から求めた２値画像の面
積を比較することにより、検出した異常が状態変化の時
定数の長い異常か状態変化の時定数の短い異常かを判定
することが可能となる。FIG. 4 is an example in which the present invention is applied to an abnormality having a long time constant of a state change such as a vapor leak and the abnormality type of the fluid leakage is determined. The image 12a is the result of detecting an abnormality by binarizing the difference image 11c that has undergone difference processing in a short difference cycle. An abnormality is detected as a binary image 83a on the difference image 12a. The image 12b is the result of detecting an abnormality by binarizing the difference image 11c that has been subjected to the difference processing in a long difference cycle. An abnormality is detected as a binary image 83b on the difference image 12b. Here, comparing the areas of the binary images in this case, it can be seen that the difference cycle is larger. As described above, in the case of an abnormality such as a steam leak having a long time constant of a state change, the area of the binary image obtained by the difference processing with a large difference cycle is larger. On the other hand, in the case of an abnormality with a short time constant of state change such as water leakage, the area of the binary image obtained by the difference processing with a small difference cycle is larger. By comparing the areas of the binary images obtained from the difference processing with the difference cycle changed in this way, it is possible to determine whether the detected abnormality is an abnormality with a long time constant of state change or an abnormality with a short time constant of state change. Is possible.
【００１０】次に、本発明の一実施例の漏洩検出方法を
図５〜図６を用いて説明する。図５は本発明の一実施例
の漏洩検出方法の概略フローチャートである。まず、差
分周期を数１００ｍｓ程度の小さな値に設定し、画像間
の差分処理をすることにより、異常を２値画像として検
出し、その２値画像の面積を求める(ステップ１０Ａ)。
次に、差分周期を数秒程度の大きな値に設定し、画像間
の差分処理をすることにより、異常を２値画像として検
出し、その２値画像の面積を求める（ステップ１０
Ｂ）。ここで求めた２値画像の面積が所定値以上かどう
か判定する（ステップ１０Ｃ）。この異常判定により、
面積が所定値未満の場合は異常なしと判定する（ステッ
プ１０Ｅ）。また、面積が所定値以上の場合は次のステ
ップを実行する。このステップでは差分周期が異なる差
分処理から求めた２値画像同志の面積を比較する（ステ
ップ１０Ｄ）。ここで、差分周期大の差分処理で求めた
２値画像の面積の方が大きい場合は状態変化の時定数の
長い異常（蒸気漏れなど）であるとする（ステップ１０
Ｆ）。一方、差分周期小の差分処理で求めた２値画像の
面積の方が大きい場合は状態変化の時定数の短い異常
（水漏れなど）であるとする（ステップ１０Ｇ）。図６
は本発明の一実施例の漏洩検出方法を補足説明するため
の図である。これは漏洩検出方法のなかで述べた時系列
画像の差分処理のフローチャートである。まず、時刻ｔ
における画像を取り込む（ステップ２０Ａ）。次に、一
定時間Δｔだけ時間待ちを行い（ステップ２０Ｂ）、時
刻ｔ＋Δｔにおける画像を取り込む（ステップ２０
Ｃ）。このΔｔが差分処理における差分周期であり、任
意の値に設定できる。さらにこの取り込んだ画像同志の
差分処理を行う（ステップ２０Ｄ）。ここで、得られた
差分画像を所定のしきい値で２値化し（ステップ２０
Ｅ)、この２値画像を順次、累積していく(ステップ２０
Ｆ）。次に２値画像の累積を続けるか判定し、続ける場
合はステップ２０Ａ〜ステップ２０Ｆの処理を実行する
（ステップ２０Ｇ）。最終的に累積した２値画像の面積
を求める（ステップ２０Ｈ）。このようにして、時系列
画像の差分処理により、異常を２値画像として検出する
ことができる。Next, a leak detecting method according to an embodiment of the present invention will be described with reference to FIGS. FIG. 5 is a schematic flowchart of a leak detection method according to an embodiment of the present invention. First, the difference cycle is set to a small value of about several hundred ms and the difference processing between images is performed to detect an abnormality as a binary image, and the area of the binary image is obtained (step 10A).
Next, the difference cycle is set to a large value of about several seconds, and the difference processing between the images is performed to detect the abnormality as a binary image, and the area of the binary image is obtained (step 10).
B). It is determined whether the area of the binary image obtained here is equal to or larger than a predetermined value (step 10C). By this abnormality judgment,
If the area is less than the predetermined value, it is determined that there is no abnormality (step 10E). If the area is equal to or larger than the predetermined value, the next step is executed. In this step, the areas of the binary images obtained by the difference processing with different difference cycles are compared (step 10D). Here, when the area of the binary image obtained by the difference process with the large difference cycle is larger, it is assumed that the state change has a long time constant (vapor leakage or the like) (step 10).
F). On the other hand, when the area of the binary image obtained by the difference process with the small difference cycle is larger, it is determined that the abnormality is a short time constant of state change (such as water leakage) (step 10G). Figure 6
FIG. 6 is a diagram for supplementarily explaining a leak detection method according to an embodiment of the present invention. This is a flowchart of the time series image difference processing described in the leak detection method. First, time t
The image is captured (step 20A). Next, waiting is performed for a fixed time Δt (step 20B), and the image at time t + Δt is captured (step 20).
C). This Δt is the difference cycle in the difference processing and can be set to any value. Further, the difference processing of the captured images is performed (step 20D). Here, the obtained difference image is binarized by a predetermined threshold value (step 20
E), this binary image is sequentially accumulated (step 20).
F). Next, it is determined whether or not the accumulation of binary images is to be continued, and if so, the processing of steps 20A to 20F is executed (step 20G). The area of the finally accumulated binary image is obtained (step 20H). In this way, the abnormality can be detected as a binary image by the difference processing of the time series images.
【００１１】次に本発明の一実施例の流体漏洩検出装置
について説明する。図１は本発明の一実施例のブロック
図である。可視カメラ１で撮像した監視対象８の画像は
画像入力部２を介して演算部３に入力される。画像入力
部２では可視カメラ１からの映像信号の前処理を行う。
差分周期設定部４では差分周期を設定し、その情報を演
算部３に出力する。演算部３では差分周期に基づいて画
像間の差分処理や２値化処理などの演算を行い、演算結
果は画像メモリ５で記憶する。画像メモリ５では演算結
果の他取り込んだ濃淡画像なども記憶する。異常判定部
６では演算により求めた２値画像同志の面積を比較し、
異常の種別を判定する。表示部７では画像間の演算処理
により求めた異常の２値画像及び異常の種別などについ
て表示する。Next, a fluid leakage detecting device according to an embodiment of the present invention will be described. FIG. 1 is a block diagram of an embodiment of the present invention. An image of the monitoring target 8 captured by the visible camera 1 is input to the calculation unit 3 via the image input unit 2. The image input unit 2 pre-processes the video signal from the visible camera 1.
The difference cycle setting unit 4 sets a difference cycle and outputs the information to the calculation unit 3. The calculation unit 3 performs calculation such as difference processing between images and binarization processing based on the difference cycle, and the calculation result is stored in the image memory 5. The image memory 5 stores the captured grayscale image in addition to the calculation result. The abnormality determination unit 6 compares the areas of the binary images obtained by the calculation,
Determine the type of anomaly. The display unit 7 displays a binary image of an abnormality and a type of the abnormality, which are obtained by calculation processing between the images.
【００１２】次に本発明の応用例について説明する。図
７は本発明の流体漏洩検出装置をプラントの異常監視装
置として応用した実施例である。プラント４０には監視
対象４５の異常を検知するためにレール４１が設置して
ある。監視装置４３は駆動機構４２に搭載してあり、レ
ール４１上を移動して監視対象４５の異常監視を行う。
監視装置４３で検出したデータはプラント外に設置した
データ処理装置４４に送られ、監視対象４５の流体漏洩
の有無及び異常の種別を特定する。Next, application examples of the present invention will be described. FIG. 7 shows an embodiment in which the fluid leakage detection device of the present invention is applied as a plant abnormality monitoring device. A rail 41 is installed in the plant 40 to detect an abnormality in the monitoring target 45. The monitoring device 43 is mounted on the drive mechanism 42 and moves on the rail 41 to monitor the abnormality of the monitoring target 45.
The data detected by the monitoring device 43 is sent to the data processing device 44 installed outside the plant, and the presence or absence of fluid leakage of the monitoring target 45 and the type of abnormality are specified.
【００１３】[0013]
【発明の効果】以上、説明したように本発明によれば、
次のような効果がある。As described above, according to the present invention,
It has the following effects.
【００１４】本発明の漏洩検出方法及び装置によれば、
差分周期大の差分処理で求めた異常検出面積と差分周期
小の差分処理で求めた異常検出面積を比較する手法によ
り、流体漏洩に伴う異常の種別の自動判定が可能とな
る。また、異なる差分周期の差分処理をそれぞれ実施す
る手法により、状態変化の時定数に関わらず高感度で異
常を検出することが可能となる。According to the leakage detection method and apparatus of the present invention,
By the method of comparing the abnormality detection area obtained by the difference processing with the large difference cycle with the abnormality detection area obtained by the difference processing with the small difference cycle, it is possible to automatically determine the type of abnormality due to fluid leakage. Further, by the method of performing the difference processing of different difference cycles, it becomes possible to detect the abnormality with high sensitivity regardless of the time constant of the state change.
【図１】本発明の実施例の流体漏洩検出装置のブロック
図である。FIG. 1 is a block diagram of a fluid leakage detection device according to an embodiment of the present invention.
【図２】本発明の概要を説明するための図である。FIG. 2 is a diagram for explaining the outline of the present invention.
【図３】本発明の概要を説明するための図である。FIG. 3 is a diagram for explaining the outline of the present invention.
【図４】本発明の概要を説明するための図である。FIG. 4 is a diagram for explaining the outline of the present invention.
【図５】本発明の実施例の漏洩検出方法の概略フローチ
ャートである。FIG. 5 is a schematic flowchart of a leak detection method according to an embodiment of the present invention.
【図６】本発明の実施例の漏洩検出方法を補足説明する
ための図である。FIG. 6 is a diagram for supplementarily explaining the leak detection method according to the embodiment of the present invention.
【図７】本発明の応用例のプラント監視装置を説明する
ための図である。FIG. 7 is a diagram for explaining a plant monitoring apparatus according to an application example of the present invention.
１…可視カメラ、３…演算部、４…差分周期設定部、５
…画像メモリ、６…異常判定部、７…表示部。1 ... Visible camera, 3 ... Calculation unit, 4 ... Difference period setting unit, 5
... Image memory, 6 ... Abnormality determination section, 7 ... Display section.
Claims (3)
手段と該手段により求めた画像を用いて少なくとも２つ
以上の異なる周期の差分処理により異常を検出する手段
と求めた異常の面積を比較する手段とを備えたことを特
徴とする漏洩検出方法。1. An area of an anomaly obtained is compared with a means for measuring the condition of a monitoring device as a visible image and a means for detecting anomaly by differential processing of at least two or more different periods using the image obtained by the means. And a means for performing the leakage detection method.
部とこの撮像した画像を演算処理する演算部を有する監
視装置において、少なくとも２つ以上の異なる周期を設
定するための差分周期設定部と少なくとも２つ以上の異
なる周期で差分処理して求めた異常の面積を比較する異
常判定部を付加したことを特徴とする漏洩検出装置。2. A difference cycle setting unit for setting at least two or more different cycles in a monitoring device having an image pickup unit for measuring the state of a monitoring device as an image and a calculation unit for arithmetically processing the picked-up image. A leakage detection apparatus, further comprising an abnormality determination unit for comparing the area of an abnormality obtained by performing difference processing in at least two or more different cycles.
内機器の異常検出手段として構成したことを特徴とする
プラントの異常監視装置。3. An abnormality monitoring device for a plant, characterized in that the leakage detecting device according to claim 2 is configured as an abnormality detecting means for equipment in the plant.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP13157994A JPH07333171A (en) | 1994-06-14 | 1994-06-14 | Leak detection method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP13157994A JPH07333171A (en) | 1994-06-14 | 1994-06-14 | Leak detection method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH07333171A true JPH07333171A (en) | 1995-12-22 |
Family
ID=15061358
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP13157994A Pending JPH07333171A (en) | 1994-06-14 | 1994-06-14 | Leak detection method and device |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH07333171A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000310577A (en) * | 1999-04-27 | 2000-11-07 | Toshiba Corp | Device and method for measuring amount of leakage |
| JP2008039479A (en) * | 2006-08-02 | 2008-02-21 | Nitto Denko Corp | Method and apparatus for inspecting water leakage of breathable membrane, and method for manufacturing breathable member |
| CN101832470A (en) * | 2010-05-19 | 2010-09-15 | 中国船舶重工集团公司第七〇二研究所 | Method and device for polling underwater lines based on light vision sensing |
| CN102128351A (en) * | 2010-01-14 | 2011-07-20 | 管丽环境技术(上海)有限公司 | Sonar detection method for functional state of pipeline |
| WO2013035846A1 (en) * | 2011-09-08 | 2013-03-14 | サントリーホールディングス株式会社 | Container liquid leak inspection device |
| CN105465611A (en) * | 2015-11-16 | 2016-04-06 | 武汉中仪物联技术股份有限公司 | Sonar detection method for water drainage pipeline |
| WO2018087821A1 (en) * | 2016-11-09 | 2018-05-17 | 株式会社オプティム | Inspection system, inspection method, inspection device, and program |
| KR102601916B1 (en) * | 2023-01-25 | 2023-11-14 | 한국기계연구원 | Image base pipe damage detecting system and method for detecting pipe damage using the same |
-
1994
- 1994-06-14 JP JP13157994A patent/JPH07333171A/en active Pending
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000310577A (en) * | 1999-04-27 | 2000-11-07 | Toshiba Corp | Device and method for measuring amount of leakage |
| JP2008039479A (en) * | 2006-08-02 | 2008-02-21 | Nitto Denko Corp | Method and apparatus for inspecting water leakage of breathable membrane, and method for manufacturing breathable member |
| CN102128351A (en) * | 2010-01-14 | 2011-07-20 | 管丽环境技术(上海)有限公司 | Sonar detection method for functional state of pipeline |
| CN101832470A (en) * | 2010-05-19 | 2010-09-15 | 中国船舶重工集团公司第七〇二研究所 | Method and device for polling underwater lines based on light vision sensing |
| WO2013035846A1 (en) * | 2011-09-08 | 2013-03-14 | サントリーホールディングス株式会社 | Container liquid leak inspection device |
| JP2013057602A (en) * | 2011-09-08 | 2013-03-28 | Suntory Holdings Ltd | Liquid leak checkup device for containers |
| EP2755006A4 (en) * | 2011-09-08 | 2015-08-12 | Suntory Holdings Ltd | Container liquid leak inspection device |
| CN105465611A (en) * | 2015-11-16 | 2016-04-06 | 武汉中仪物联技术股份有限公司 | Sonar detection method for water drainage pipeline |
| WO2018087821A1 (en) * | 2016-11-09 | 2018-05-17 | 株式会社オプティム | Inspection system, inspection method, inspection device, and program |
| KR102601916B1 (en) * | 2023-01-25 | 2023-11-14 | 한국기계연구원 | Image base pipe damage detecting system and method for detecting pipe damage using the same |
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