JPH02201133A - Sensing method of distribution type optical fiber sensor - Google Patents
Sensing method of distribution type optical fiber sensorInfo
- Publication number
- JPH02201133A JPH02201133A JP1019996A JP1999689A JPH02201133A JP H02201133 A JPH02201133 A JP H02201133A JP 1019996 A JP1019996 A JP 1019996A JP 1999689 A JP1999689 A JP 1999689A JP H02201133 A JPH02201133 A JP H02201133A
- Authority
- JP
- Japan
- Prior art keywords
- data group
- averaging processing
- main data
- change
- optical fiber
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 239000013307 optical fiber Substances 0.000 title claims abstract description 26
- 238000009826 distribution Methods 0.000 title abstract description 25
- 238000012935 Averaging Methods 0.000 claims abstract description 82
- 238000012545 processing Methods 0.000 claims abstract description 72
- 238000005259 measurement Methods 0.000 claims abstract description 39
- 238000005070 sampling Methods 0.000 claims abstract description 9
- 238000000253 optical time-domain reflectometry Methods 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 11
- 230000003287 optical effect Effects 0.000 description 8
- 238000001514 detection method Methods 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000001069 Raman spectroscopy Methods 0.000 description 3
- 230000003321 amplification Effects 0.000 description 3
- 238000009529 body temperature measurement Methods 0.000 description 3
- 238000003199 nucleic acid amplification method Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 238000005309 stochastic process Methods 0.000 description 2
- PCTMTFRHKVHKIS-BMFZQQSSSA-N (1s,3r,4e,6e,8e,10e,12e,14e,16e,18s,19r,20r,21s,25r,27r,30r,31r,33s,35r,37s,38r)-3-[(2r,3s,4s,5s,6r)-4-amino-3,5-dihydroxy-6-methyloxan-2-yl]oxy-19,25,27,30,31,33,35,37-octahydroxy-18,20,21-trimethyl-23-oxo-22,39-dioxabicyclo[33.3.1]nonatriaconta-4,6,8,10 Chemical compound C1C=C2C[C@@H](OS(O)(=O)=O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2.O[C@H]1[C@@H](N)[C@H](O)[C@@H](C)O[C@H]1O[C@H]1/C=C/C=C/C=C/C=C/C=C/C=C/C=C/[C@H](C)[C@@H](O)[C@@H](C)[C@H](C)OC(=O)C[C@H](O)C[C@H](O)CC[C@@H](O)[C@H](O)C[C@H](O)C[C@](O)(C[C@H](O)[C@H]2C(O)=O)O[C@H]2C1 PCTMTFRHKVHKIS-BMFZQQSSSA-N 0.000 description 1
- WLNBMPZUVDTASE-HXIISURNSA-N (2r,3r,4s,5r)-2-amino-3,4,5,6-tetrahydroxyhexanal;sulfuric acid Chemical compound [O-]S([O-])(=O)=O.O=C[C@H]([NH3+])[C@@H](O)[C@H](O)[C@H](O)CO.O=C[C@H]([NH3+])[C@@H](O)[C@H](O)[C@H](O)CO WLNBMPZUVDTASE-HXIISURNSA-N 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 238000009933 burial Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Landscapes
- Measuring Temperature Or Quantity Of Heat (AREA)
Abstract
Description
【発明の詳細な説明】
［産業上の利用分野１
本発明は光ファイバの後方散乱光を利用した、分布形光
ファイバセンサのセンシング方法に関するものである。DETAILED DESCRIPTION OF THE INVENTION [Industrial Application Field 1] The present invention relates to a sensing method using a distributed optical fiber sensor that utilizes backscattered light from an optical fiber.
［従来の技術］
光ファイバーｇＪから光パルスを入射し、後方散乱光を
検出するＯ Ｔ Ｄ Ｒ（Ｏｐｔｉｃａｌ ＴｉＩｅ
Ｄｏｎａ＋ｎＲｅｆ Ｉｅｃｔｏｎｅｔｒｙ）手法を用
いた分布形センサがある。[Prior art] OTD R (Optical TiIe) in which a light pulse is input from an optical fiber gJ and backscattered light is detected.
There is a distributed sensor using the Dona+nRef Iectonetry) method.
特に温度センサとして後方散乱光の中でもレーレ散乱や
ラマン敗乱光の利用が盛んである。即ち光ファイバの長
手方向各部ではその温度に対応した散乱が生じるので、
短時間幅のパルスを入射し、それが光ファイバを伝播し
て行く時に各部で発生する後方散乱光を入射ｒ４側で時
間変化として捕らえ、ファイバ中の光伝播速度を用いて
距離に換算すると、光ファイバに沿った各部の散乱光の
分布、即ち温度分布を求めることができる。In particular, among backscattered light, Lehre scattering and Raman scattered light are widely used as temperature sensors. In other words, since scattering occurs at each part in the longitudinal direction of the optical fiber in accordance with its temperature,
When a short-time pulse is input and the backscattered light generated at each part as it propagates through the optical fiber is captured as a time change on the input r4 side and converted to distance using the light propagation speed in the fiber, we get: The distribution of scattered light at each part along the optical fiber, that is, the temperature distribution can be determined.
しかし、後方散乱光は極めて微弱であるため、光信号段
階やこれを電気信号に変換する時、或いは電気信号段階
において外雑の影響を受は易いこと、また散乱光発生は
確率過程によること等のために、単一パルスによる後方
散乱光のみでは、温度分布を求めることは出来ない、こ
のため、多数回の光パルスに対する受信信号のタイミン
グを合わせた平均値を求めることにより、光ファイバに
沿った温度を求める手法が採られる。このような多数回
の平均化処理は、ごく単純に光ファイバの損失を測定す
るための０ＴＤＲでも採用されており、１０４回程度の
レーレ散乱光の平均化処理を行っているのが普通である
。However, because backscattered light is extremely weak, it is easily affected by external interference at the optical signal stage, when converting it to an electrical signal, or at the electrical signal stage, and the generation of scattered light is due to a stochastic process. Therefore, it is not possible to determine the temperature distribution using only the backscattered light from a single pulse. Therefore, by determining the average value of the timing of the received signal for multiple optical pulses, it is possible to calculate the temperature distribution along the optical fiber. A method is used to find the temperature. This type of averaging process performed many times is also used in 0TDR, which is used to simply measure the loss of optical fibers, and it is common to average Lehle scattered light about 104 times. .
温度検出のための後方散乱光のうち、ラマン敗乱光はレ
ーレ散乱光の約１／１０００程度の微弱光であるため、
ますます外雑及び確率過程の影響を受は易く、ある程度
の精度を確保するには１０６回以上の平均化処理を行う
必要が生じる場合がある。Of the backscattered light for temperature detection, Raman scattered light is weak light that is about 1/1000 of Lehre scattered light.
It is increasingly susceptible to the influence of extraneous noise and stochastic processes, and it may be necessary to perform averaging processing 106 times or more to ensure a certain degree of accuracy.
光フアイバセンサの対象温度が時間的に安定している場
合には、必要回数の平均化処理を行った後、温度分布を
出力すれば十分な精度で測定が可能となる。If the target temperature of the optical fiber sensor is stable over time, measurement can be performed with sufficient accuracy by outputting the temperature distribution after performing averaging processing a necessary number of times.
［発明が解決しよとする課Ｊ！ｌ！］
しかし、対象温度が所定の平均化処理を行うのに要する
時間内で変化する場合には、その時間内の平均温度とし
ては所期の精度で測定できるが、実際の対象物の温度と
の比較においては、大きな誤差を生じることになる。逆
に、短時間での温度変化にある程度追随した温度分布を
得る目的で平均化処理回数を減らすと、対象物の温度変
化にはある程度追随できるが、対象物の温度変化が小さ
いか一定温度が保たれている場合の測定精度が落ちる。[Section J that inventions aim to solve! l! ] However, if the target temperature changes within the time required to perform the predetermined averaging process, the average temperature within that time can be measured with the desired accuracy, but it may differ from the actual temperature of the target. A large error will occur in the comparison. Conversely, if you reduce the number of averaging processes in order to obtain a temperature distribution that follows temperature changes to some extent over a short period of time, you can follow the temperature changes of the target object to some extent, but if the temperature change of the target object is small or the temperature is constant. Measurement accuracy decreases when the temperature is maintained.
現用の各種０ＴＤＲ手法では、平均化処理回数を手動で
変更することは可能であるが、元来が処理時間と同等オ
ーダで刻々と時間変化する測定を対象とはしていないた
め、温度分布の測定等、特に時間変化を伴う物理量分布
を自動的に適確に測定することができない。In the various current 0TDR methods, it is possible to manually change the number of averaging processes, but they are not originally intended for measurements that change moment by moment on the same order as the processing time, so it is not possible to change the temperature distribution. Measurements, etc., especially physical quantity distributions that change over time cannot be automatically and accurately measured.
本発明の目的は、従来の後方数置光検出による測定の精
度と時間変化の把握という矛盾を解決し、時間変化に伴
い実質的に精度の高い分布形光ファイバセンサのセンシ
ング方法を提供することにある。An object of the present invention is to solve the contradiction between the accuracy of measurement using conventional backward light detection and the grasp of changes over time, and to provide a sensing method using a distributed optical fiber sensor that has substantially high accuracy over changes over time. It is in.
［課題を解決するための手Ｐｌ］
本発明の分布形光ファイバセンサのセンシング方法は、
基本的には、位置及び時１ｍにより変化する物理量を光
ファイバの後方散乱光を利用して測定するセンシングシ
ステムにおいて、パルス入射光に対する後方散乱光をサ
ンプリングし、同一サンプリングタイミングに相当する
データを逐次多数回平均化処理するに当って、一連のサ
ンプリングと物理量の変化が時間的に緩やかな状態での
測定精度が高い平均化処理回数Ｎｏよりも回数が少ない
平均化処Ｆｌ！回数Ｎ＋ とを行って得たデータ群Ｄ１
を平均化処理選択手段に導き、該手段において、このデ
ータ群Ｄ１をその直前に得ている主データ群Ｄｏとを対
応するデータ毎に対比して差分ΔＤを作成し、その差分
ΔＤを判定レベルと比較し、差分ΔＤと判定レベルより
の大小関係により、物理量の変化が時間的に緩やかな状
態のときは、上記Ｎ１回処理のデータ群Ｄ１とその直前
に得ている主データ群Ｄｏの回数の重味を考慮した平均
化処理により新たなデータ群を得て、これを新たな主デ
ータ群として測定を行い、物理量の時間変化が急激な状
態のときは、上記Ｎ１回処理のデータ群Ｄ１を新たな主
データ群として測定を行うものである。[Measures to solve the problem Pl] The sensing method of the distributed optical fiber sensor of the present invention is as follows:
Basically, in a sensing system that uses backscattered light from an optical fiber to measure physical quantities that change depending on position and time, 1m, the backscattered light for pulsed incident light is sampled, and data corresponding to the same sampling timing is sequentially collected. When performing averaging processing many times, the averaging processing Fl! is performed less often than the averaging processing number No. which has high measurement accuracy in a state where a series of samplings and changes in physical quantities are gradual over time. Data group D1 obtained by performing N+ times
is guided to an averaging processing selection means, and in the means, a difference ΔD is created by comparing this data group D1 with the main data group Do obtained immediately before for each corresponding data, and the difference ΔD is set as a judgment level. When the change in the physical quantity is gradual over time due to the magnitude relationship between the difference ΔD and the judgment level, the number of times of the data group D1 processed N1 times and the main data group Do obtained immediately before A new data group is obtained through averaging processing that takes into account the weight of is measured as a new main data group.
より好ましい形態としては、判定レベルを５１３２
（Ｓｌ ＜Ｓ２ ）の２種とし、Ｎ１回処理のデータ群
Ｄ＋ とその直前に得ている主データ群ＤＯとを対比し
て得た上記差分ΔＤを判定レベルＳ＋８２と比較し、Δ
Ｄ＜３＋のときには、Ｎ１回処理のデータ群Ｄ＋ とそ
の直前に得ている主データ群の回数の重味を考慮した平
均化処理により新たなデータ群を得て、これを新たな主
データ群とし、物理量の変化が時間的によりＭやかな状
態での高精度認定を行い、またΔＤ＞８２のときは、Ｎ
１回処理のデータ群を新たな主データ群として、物理量
の時間変化がより急激な状態での高精度測定を行い、更
にＳ１≦ΔＤ≦Ｓ２のときには、その継続性により上記
いずれかの処理により、新たなデータ群を得て、物理量
の時間変化が中間的な状態での高精度測定を行う、この
場合、判断レベルの設定は、定常状態での理論上の測定
誤差として、Ｎ１回処理時の誤差に１とＮｏ見回処理時
誤差ＫＯとの間に、Ｋ＋ ｃｃ（Ｗ”；）”ＷＴ −Ｋ
ｏが成り立つ関係において、上記判断レベルをＳｌ ＝
Ｋｏ 。As a more preferable form, the judgment level is set to 5132.
(Sl < S2), and the difference ΔD obtained by comparing the data group D+ processed N1 times with the main data group DO obtained immediately before that is compared with the judgment level S+82, and Δ
When D<3+, a new data group is obtained by averaging processing that takes into account the weight of the data group D+ processed N1 times and the main data group obtained immediately before, and this is used as a new main data group. Then, high accuracy certification is performed under conditions where physical quantity changes are more rapid over time, and when ΔD>82, N
Using the data group processed once as a new main data group, high-precision measurement is performed in a state where physical quantities change more rapidly over time, and when S1≦ΔD≦S2, one of the above processes is performed due to the continuity. , obtain a new data group and perform high-precision measurement in a state where the time change of the physical quantity is intermediate. In this case, the judgment level is set as a theoretical measurement error in a steady state, and N1 processing times. Between the error of 1 and the error KO when processing No., K + cc(W”;)”WT −K
In the relationship where o holds, let the above judgment level be Sl =
Ko.
Ｓ２ ＝に＋ と設定することができる。S2= can be set as +.
［作用］
平均化処理選択手段は、０ＴＤＲ信号の平均化処理回数
を比較的少ない一定回数とする、即ち短時間毎に現信号
を得る、ということにより、現信号と、記憶されている
その直前のデータとを比較し、現データ作成の平均化処
理回数を選択する。[Operation] The averaging processing selection means sets the number of averaging processings of the 0TDR signal to a relatively small fixed number of times, that is, obtains the current signal every short period of time, so that the current signal and the stored immediately preceding , and select the number of times of averaging processing to create the current data.
それによって物理量の時間変化のＭ肉を的確に把握する
と共に、刻々の変化を最も高い精度で測定することを可
能にする。This makes it possible to accurately grasp the time variation of physical quantities and to measure momentary changes with the highest precision.
平均化処Ｐｌ！選択手段がなければ、物理量の変化が時
間的に緩やかな状態での測定精度が高い平均化処理回数
Ｎｏにより処理すると、物理量の時間変化が急激な状態
に追随することが元来困難となる。しかし、このような
状態のときは、本平均化処理選択手段が、データ群Ｄ１
とその直前に得ている主データ群Ｄｏとの差分ΔＤを判
定レベルに対する大小関係より、物理量の変化が時間的
に緩やかな状態と判定する。そして、上記Ｎ１回処理の
データ群Ｄ１とその直前に得ている主データ群Ｄｏの回
数の重味を考慮した平均化処理により新たなデータ群を
得て、これを新たな主データ群として測定を行う、この
なめ、物理量の時間変化が急激な状態に追随することが
できるようになる。Averaging process Pl! If there is no selection means, if processing is performed using an averaging processing number number with high measurement accuracy in a state where the physical quantity changes slowly over time, it will be difficult to follow a state where the physical quantity changes rapidly over time. However, in such a state, the averaging processing selection means selects the data group D1.
Based on the magnitude relationship between the difference ΔD and the main data group Do obtained immediately before that with respect to the determination level, it is determined that the change in the physical quantity is gradual over time. Then, a new data group is obtained by averaging processing that takes into consideration the weight of the number of times of the data group D1 processed N1 times and the main data group Do obtained immediately before, and this is measured as a new main data group. By doing this, we will be able to follow conditions where physical quantities change rapidly over time.
一方、平均化処理回数Ｎｏよりも回数が少ない平均化処
理回数Ｎ１は、元来平均化処理回数ＮＯの場合よりも測
定精度が落ちるが、平均化処理選択手段が、上記差分Δ
Ｄの判定レベルに対する大小関係より、物理量の時間変
化が急激な状態であるときを捕らえ、そのときだけ上記
Ｎ１回処理のデータ群Ｄ１を新たな主データ群として測
定を行わせる。このため、全体の測定精度が低下せず、
且つ、物理量の時間変化が急激な状態にも追随できるこ
ととなる。On the other hand, the number of averaging processes N1, which is smaller than the number of averaging processes No, originally has lower measurement accuracy than the case where the number of averaging processes is NO.
Based on the magnitude relationship of D with respect to the determination level, the time when the physical quantity changes rapidly is detected, and only at that time, the data group D1 processed N1 times is measured as a new main data group. Therefore, the overall measurement accuracy does not deteriorate, and
In addition, it is possible to follow a state in which the physical quantity changes rapidly over time.
［実施例］ 先ず、温度計測上の矛盾を具体例で示す。[Example] First, we will show a specific example of a contradiction in temperature measurement.
測定対象物がプラント配管システムであるとし、その中
のある点の温度が、第６図の実線で示すような時間変化
をしたとする。Assume that the object to be measured is a plant piping system, and that the temperature at a certain point within the system changes over time as shown by the solid line in FIG.
平均化処理回数が８０回のときの処理タイミングが図中
のａ ｒ ＊ ａ ２・・・ａ、、平均化処理回数がＮ
１回（Ｎｏ ＞Ｎ＋ ）の時の処理タイミングが１）ｌ
、 ｂｉ −” ｂｍ とする、 ａ （〜ａ
ｍで測定される温度は図中の黒丸と一点鎖線で示すよう
な階段状の変化を示し、またｂ１〜ｂ、の温度はＸ印と
破線で示すような変化を示すことになる。The processing timing when the number of averaging processing is 80 is a r * a 2...a in the figure, and the number of averaging processing is N.
The processing timing when 1 time (No > N+) is 1)l
, bi −”bm, a (~a
The temperature measured at m shows a step-like change as shown by the black circle and a dashed line in the figure, and the temperature at b1 to b shows a change as shown by an X mark and a broken line.
第６図中、５０℃付近がプラント正常運転の状態であり
、常時はこれを±１℃の精度で制御しており、この制御
系が誤動伴をしたとき、７０℃を越えると［異常」とし
て運転停止し、１００℃を越えると「危険」として警報
を出すことになっている。In Figure 6, around 50℃ is the state of normal plant operation, which is normally controlled with an accuracy of ±1℃, and when this control system malfunctions and the temperature exceeds 70℃ '', and if the temperature exceeds 100℃, a warning will be issued as a ``danger''.
また、平均化処理回数はＮｏ＝１０’で、これにより温
度測定誤差ＫＯが±１℃未満に保たれている。一方、平
均化処理回数がＮ＋＝１０’では、統計処理の考え方か
ら、ランダムな雑音に基づく誤差がｎ−］１Ｇに比例す
るので、測定誤差に＋は±３℃となる。Further, the number of times of averaging processing is No=10', thereby keeping the temperature measurement error KO below ±1°C. On the other hand, when the number of times of averaging processing is N+=10', the error based on random noise is proportional to n-]1G from the perspective of statistical processing, so the measurement error + is ±3°C.
このような状況で、常時の運転制御を正確に行うため、
Ｎｏ回の平均化処理（測定誤差ＫＯが±１゛Ｃ）を行っ
ていると、８２時点では７０’Ｃ未満であるので「異常
」が検出できず、本来危険となる１００℃に近い８３時
点でようやく「異常」を検出し、更にその後に「危険」
を検知することになり、「異常」 「危険」に対する処
理が手遅れとなる。In such situations, in order to accurately control operation at all times,
If the averaging process is performed No times (measurement error KO is ±1°C), the temperature at point 82 is less than 70'C, so an "abnormality" cannot be detected, and the temperature at point 83 is close to 100°C, which is dangerous. ``Abnormality'' was finally detected, and then ``Danger'' was detected.
This means that it will be too late to deal with the ``abnormality'' or ``danger''.
一方、Ｎ１回平均化処理をしていると、正常時の測定誤
差かに＋ （−±３℃）となるため、±１℃の制御が
不可能となる。しかし、ｂａ時点で「異常」を検出し、
ｂｚｏ時点で「危険」を検出することができる。尚、測
定誤差を（’Ｆうので、これらの検出時点がそれぞれｔ
）１．ｂ２＋となることもあり得るが、いずれにしても
８０回の平均化処理をしている場合の処理タイミングａ
） ＋ ａ ４よりも遥かに早く検出可能である。On the other hand, if the averaging process is performed N1 times, the measurement error during normal operation will be + (-±3°C), making it impossible to control the temperature within ±1°C. However, an "abnormality" was detected at the time of ba,
"Danger" can be detected at the time of bzo. Furthermore, since the measurement error is ('F), each of these detection points is t.
)1. It is possible that it will be b2+, but in any case, the processing timing a when averaging processing is performed 80 times.
) + a It can be detected much earlier than 4.
上記不都合に対し、以下に述べる本発明の実施例によれ
ば、正常時にはＮｏ回の平均処理と同等の測定誤差Ｋｏ
を確保でき、かつ異常な温度上昇に対してはＮ１回と同
等の早さで、これを検出することが可能となる。In order to solve the above-mentioned inconvenience, according to the embodiment of the present invention described below, during normal operation, the measurement error Ko is equivalent to No averaging processing.
It is possible to ensure this, and to detect an abnormal temperature rise as quickly as N1 times.
以下、本発明の実施例の内容を詳述する。Hereinafter, the contents of the embodiments of the present invention will be explained in detail.
第１図に示すように、０ＴＤＲ装置１、平均化処理選択
回路２、温度分布演算・表示回路３．光フアイバセンサ
４を主構成要素としており、次のような処理を行う。As shown in FIG. 1, an 0TDR device 1, an averaging process selection circuit 2, a temperature distribution calculation/display circuit 3. The optical fiber sensor 4 is the main component and performs the following processing.
先ず、０ＴＤＲ装置１では、Ｎ１回の平均化処理（測定
誤差に１は例えば±３℃）によるデータ群（場所による
変化）を基礎とする。このデータ群を０ＴＤＲ装置１の
出力とし、本発明の主体となる平均化処理選択回路２へ
入力する。First, the 0TDR device 1 is based on a data group (changes depending on location) obtained by averaging N1 times (1 for measurement error is, for example, ±3° C.). This data group is output from the 0TDR device 1 and input to the averaging processing selection circuit 2, which is the main subject of the present invention.
平均化処理選択回路２では、以下のような判定処理によ
り、適確な温度分布のためのデータを得る。尚、ここで
平均化処理回数ＮＯとＮ１ （Ｎ＋＜Ｎｏ ）は、Ｎｏ
＝ＭＮ＋ （Ｍは２以上の整数）の関係にあり、従
って、平均化処理回数Ｎｏによる測定誤差Ｋｏは、０Ｔ
ＤＲ装置１のＮ１回の平均化処理の測定誤差に１よりも
高く、例えば±１℃であるとする。The averaging process selection circuit 2 obtains data for accurate temperature distribution through the following determination process. In addition, here, the number of times of averaging processing NO and N1 (N+<No) are No.
= MN+ (M is an integer greater than or equal to 2). Therefore, the measurement error Ko due to the number of averaging processes is 0T.
It is assumed that the measurement error of N1 averaging processes of the DR device 1 is higher than 1, for example, ±1°C.
第２図を参照しながら、平均化処理選択回路２の判定処
理の仕方を説明する。With reference to FIG. 2, the method of determination processing by the averaging processing selection circuit 2 will be explained.
（１）今回、０ＴＤＲ装置１から平均化処理回数Ｎ＋（
ＮＩＰ誤差に＋ ）に基づいて得られたデータ群を受け
た時、各位置に対応したデータ毎に、その直前の最新主
データ群と比較する（第２図のステップ■）。(1) This time, the number of averaging processes N+(
When a data group obtained based on the NIP error (+) is received, each data corresponding to each position is compared with the latest main data group immediately before it (step 2 in FIG. 2).
（２） １１Ｉ定誤差を含めた温度変化に関し、予め２
つのレベル３１ Ｎ２ （Ｓｔ ＜Ｎ２ ）を設定
しておき、データ間の変化幅ΔＤにより、ΔＤ＜Ｓｔの
場合、ΔＤ＞Ｓ２の場合、Ｓ１≦ΔＤ≦８２の場合とい
う３ケースに分ける（第２図のステップ■）
ゲース１；ΔＤ＜Ｓｔの場合
全てのデータ間でΔＤ＜Ｓｔのときは、温度変化が緩慢
であり、異常温度変化の可能性が小さいので、正常時の
精確な温度演算処理をする。即ち、最新の主データ群と
今回のデータ群の各位置に対応したデータ毎に平均化処
理を行い、それを最新の主データ群とすると共に（第２
図のステップ■■■）、全ての指定ポイントを解除する
（第２図のステップ■■■）、「指定ポイント解除」と
は、それまで設定されていた後述するケース２．ゲース
３の（ｉ）ｃ目）で校定される指定ポイントを全て無と
し、「継続性無し」とすることである、平均化処理（第
２図のステップ■）における新たな主データ群の作成方
法についての詳細は後述する。(2) Regarding temperature changes including 11I constant error, please check 2 in advance.
One level 31 N2 (St < N2) is set, and it is divided into three cases depending on the change width ΔD between data: ΔD<St, ΔD>S2, and S1≦ΔD≦82 (second Step ■ in the figure) Gauge 1: When ΔD<St When ΔD<St among all data, the temperature change is slow and the possibility of abnormal temperature change is small, so accurate temperature calculation processing under normal conditions is required. do. That is, average processing is performed for each data corresponding to each position of the latest main data group and the current data group, and this is used as the latest main data group (second
(Step ■■■ in the figure), cancel all designated points (Step ■■■ in Figure 2), "Cancellation of designated points" refers to Case 2, which will be described later, that had been set up until then. For the new main data group in the averaging process (step ■ in Figure 2), all the specified points calibrated in (i) cth) of game 3 are set to zero, and there is no continuity. Details of the creation method will be described later.
ケース２：ΔＤ ＞ Ｓ ２の場合
対象とする場所で一点でもΔＤ＞Ｓ２のときは、急激な
温度変化が生じた可能性が高いので、異常な温度上昇と
してのＭＸ処理をする。即ち、今回のデータ群のみで、
その回数重味分を考慮した平均化処理を行い、これを最
新主データ群とすると共に（第２図のステップ■■■）
、ΔＤ＞Ｓ２を満したポイント（複数個ら有り得る）を
指定ポイントとして設定する（第２図のステップ■■■
）。Case 2: When ΔD>S2 If ΔD>S2 at even one point in the target location, there is a high possibility that a sudden temperature change has occurred, so MX processing is performed as an abnormal temperature rise. In other words, with only this data group,
An averaging process is performed that takes into account the weight of the number of times, and this is used as the latest main data group (step ■■■ in Figure 2).
, ΔD>S2 (there may be more than one point) is set as the designated point (step ■■■ in Figure 2).
).
ここで「指定ポイント」は、長手方向の各ポイントに相
当する時間サブリング毎の各データの順序番号のうち、
その番号のデータが本ケース２におけるΔＤ＞Ｓ２の条
件を満たした場合に、その順序番号を指定ポイントとす
る（複数個の場合も有る）。Here, the "designated point" is one of the order numbers of each data for each time subring corresponding to each point in the longitudinal direction.
When the data of that number satisfies the condition of ΔD>S2 in this case 2, that sequence number is set as a designated point (there may be multiple points).
ケース３；Ｓ１≦ΔＤ≦Ｓ２の場合
対象とする場所で一点でらＳ１≦ΔＤ≦８２のときは、
急激な温度変化が生じた可能性はあるが、測定誤差によ
るばらつきのため、正常状態を誤って温度変化があるか
のようなデータとなった可能性もある。そこで、両者の
いずれであるかを判定するための演算処理をする。即ち
、
ｍ指定ポイントが校定されていないとき、あるいは今回
Ｓ１≦ΔＤ≦Ｓ２を溝たしたポイントと指定ポイントが
全く一致しないときには、急激な温度変化によるのでは
なく測定誤差に起因するとみなし、ケース１と同じ考え
方で、今回のデータ群を加えて平均化処理を行い（第２
図のステップ■■■■■）、これを最新主データ群とす
ると共に（第２図のステップ■■）今回Ｓｌ≦ＤＯ≦８
２を満たしたポイントを指定ポイントとして設定する（
第２図のステップ■■■）、［指定ポイント」は、長手
方向の各ポイントに相当する時間サブリング毎の各デー
タの順序番号のうち、今回Ｓ１≦Ｄｏ≦８２を満たしな
順序番号を指定ポイントとする。Case 3: When S1≦ΔD≦S2 If S1≦ΔD≦82 at one point at the target location,
Although it is possible that a sudden temperature change occurred, it is also possible that due to variations due to measurement errors, the data may have been mistaken for a normal state and given the impression that there was a temperature change. Therefore, arithmetic processing is performed to determine which of the two is the case. In other words, if the m specified point has not been calibrated, or if the point where S1≦ΔD≦S2 does not coincide with the specified point at all, it is assumed that the cause is not due to a sudden temperature change but a measurement error, and the case is determined. Using the same idea as in 1, add this data group and perform averaging processing (second
Step ■■■■■ in the figure), and set this as the latest main data group (step ■■ in Figure 2) This time Sl≦DO≦8
Set the point that satisfies 2 as the specified point (
In step ■■■ in Figure 2), [designated point] specifies the sequence number that does not satisfy S1≦Do≦82 this time among the sequence numbers of each data for each time subring corresponding to each point in the longitudinal direction. Make it a point.
（１１）指定ポイントが設定されており、今回Ｓ１≦Δ
Ｄ≦Ｓ２を満たしたポイントと指定ポイントが１組以上
一致したときは、その位置で急激な温度変化の傾向が続
いているとみなし、それを新たな指定ポイントとして設
定すると共に（第２図のステップ■■［相］［相］■）
、ケース２と同じく今回のデータ群のみで、その回数重
味分を考慮した平均化処理を行い、これを最新主データ
群とする（第２図のステップ［相］■■）、ここでは、
指定ポイントは複数の場合があり得る。(11) The specified point is set, and this time S1≦Δ
When one or more pairs of specified points match points that satisfy D≦S2, it is assumed that the trend of rapid temperature change continues at that position, and this point is set as a new specified point (as shown in Figure 2). Step■■[phase][phase]■)
, As in Case 2, only this data group is subjected to averaging processing that takes into account the weight of the number of times, and this is made the latest main data group (step [phase] ■■ in Figure 2).Here,
There may be multiple designated points.
（３）以下、（１）〜（２）を繰り返す、かくして平均
化処理選択回路２で最新主データ群を得る。(3) Hereafter, (1) and (2) are repeated, thus obtaining the latest main data group in the averaging process selection circuit 2.
この最新主データ群を用いて、温度分布演算・表示回路
３により、適宜温度分布を得る。尚、ケース１（ΔＤ＜
３＋ ）の場合は、後述のしく新たな主データ群を作る
べくなされるＮ１回処理データ群の平均化処理回数）が
Ｌ＝ ２〜Ｍの任意の所で温度分布を得れば良く、Ｌ＝
Ｍ以降はＮｏ回相当の平均化処理回数でデータを得る。Using this latest main data group, the temperature distribution calculation/display circuit 3 obtains an appropriate temperature distribution. In addition, case 1 (ΔD<
In the case of 3+), it is sufficient to obtain the temperature distribution at any point between L=2 and M (the number of times of averaging processing of the data group processed N1 times to create a new main data group, which will be described later). =
After M, data is obtained by averaging the number of times equivalent to No times.
ここで、ケース１〜ゲース３の平均化処理で新たな主デ
ータ群を作るときは、次のようにして行う。Here, when creating a new main data group by averaging processing of cases 1 to 3, it is performed as follows.
（４）ケース１及びケース３の（ｉ）の場合、即ち第２
図のステップ■■の場合には、それまでの主データ群が
Ｎ１回処理データ群のＬ回分の平均化処理からなってい
るとすると、
（（それまでの主データ群の多値）ＸＬ十（今回のＮ１
回処理のデータ群の多値））／（Ｌ＋１ン
という（Ｌ＋１＞回分の平均化処理を行って作る。(4) Case 1 and case 3 (i), that is, the second
In the case of step ■■ in the figure, if the main data group up to that point consists of averaging processing of the data group processed N1 times, then ((multi-values of the main data group up to that point) XL (This time N1
It is created by performing averaging processing for (L+1>times) called multivalued data group)/(L+1 times).
Ｌ＝Ｍの場合には、ＬをＭ−１としてＭ回分の平均化処
理により作る。When L=M, it is created by averaging M times with L being M-1.
（５）一方、ケース２及びケース３の（ｉｉ）の場合、
即ち第２図のステップ■の場合には、今回のＮ＋回処理
のデータ群により、そのまま新たな主データ群を作る。(5) On the other hand, in case 2 and case 3 (ii),
That is, in the case of step (2) in FIG. 2, a new main data group is created as is from the data group processed N+ times this time.
以上の０ＴＤＲ装置の処理、上記（１）〜（３）の処理
を、上記（４）（５）の条件で実行すると、以下のよう
な効果が得られる。When the above 0TDR device processes, the processes (1) to (3) above, are executed under the conditions (4) and (5) above, the following effects can be obtained.
（ａ）測定誤差を含めて温度変化がＳＩ相当以下の場合
（ケース１の場合）には、Ｎ０回平均化処理相当、即ち
測定誤差Ｋｏ （例えば上記±１℃）以下の高精度で
温度測定ができる。(a) If the temperature change including the measurement error is less than the SI equivalent (case 1), the temperature is measured with high accuracy equivalent to the N0 averaging process, that is, the measurement error Ko (e.g. ±1°C above) or less. Can be done.
（ｂ） ｌ定誤差を含めて温度変化が８２相当以上の場
合（ケース２の場合）には、Ｎ１回処理の時間、即ちＮ
ｏ回処理の場合の１／Ｍの短時間で温度変化を把握でき
、またその実質的測定精度をＮｏ回処理で後刻に測定さ
れるはずのものより小さくすることができる。(b) If the temperature change including l constant error is equivalent to 82 or more (case 2), the time for N1 processing, that is, N
Temperature changes can be ascertained in a short time of 1/M compared to the o-times process, and the actual measurement accuracy can be made smaller than that which would be measured later in the no-times process.
（ｃ） ｉｌｌ定誤差を考慮すると、温度が変化してい
るか否か不確かであるＳｌと８２間になる場合（ケース
３の場合）では、その温度が変化する傾向が１回だけの
場合には誤差と見なし、その傾向が続いている（指定ポ
イントが一致）場合には、温度変化あつと見なして、そ
れぞれに適した平均化処理で、より高い精度の温度測定
ができる。(c) Considering the ill constant error, in the case where it is uncertain whether the temperature is changing or not, when it is between SL and 82 (case 3), if the temperature tends to change only once, It is regarded as an error, and if the trend continues (specified points match), it is assumed that there is a temperature change, and temperature measurement can be performed with higher precision by averaging processing appropriate for each.
（ｄ）急激な温度変化が終息しつつあるときには、平均
化処理回数を増して、測定精度を高精度の測定誤差Ｋｏ
の状態へ近付けることができる。(d) When rapid temperature changes are coming to an end, increase the number of averaging processes to reduce the measurement accuracy to a high-precision measurement error.
It is possible to approach the state of
ｆｅ）その時点の温度に相当する主データ群は１紐のみ
で済むので、温度分布演算回路に要する記憶呈が少なく
なる。尚、記憶装置の量が十分確保されている場合には
、Ｎ１回平均化処理による過去のデータ群（最大Ｍ組）
をそれぞれを記憶させておき、その中から適宜取り出し
て（４）（５）の平均化処理を行うことができる。fe) Since only one string is required for the main data group corresponding to the temperature at that time, the memory required for the temperature distribution calculation circuit is reduced. In addition, if the amount of storage device is sufficiently secured, past data groups (maximum M groups) obtained by averaging N1 times
It is possible to memorize each of them, take them out as appropriate, and perform the averaging process of (4) and (5).
以上のように本発明によれば、時間的、場所的に変化す
る対象物の温度を、その状況に応じて精度よく測定する
ことが可能となる。As described above, according to the present invention, it is possible to accurately measure the temperature of an object, which changes over time and location, depending on the situation.
尚、上記の実施例の他、次のような方法を採ることもで
きる。In addition to the above embodiments, the following method may also be adopted.
（１）平均化処理回数を３組以上、Ｎｏ ＞Ｎ＋ ＞Ｎ
２・・・、あるいは判定レベルをＳｌ 、Ｎ２ 、Ｎ３
・・・と設定しておき、Ｎ＋ 、Ｎ２ ；Ｎ２ 、Ｎ
３を用いて上記手法を適用し、その主データ群に対し、
Ｎｏ 、Ｎ１ ；Ｓｌ 、８２を適用すること等により
。(1) Number of times of averaging processing is 3 or more, No >N+ >N
2...or the judgment level is Sl, N2, N3
..., N+, N2; N2, N
Apply the above method using 3, and to the main data group,
By applying No, N1; Sl, 82, etc.
更に測定精度且つ時間変化の検出を容易にすることがで
きる。Furthermore, measurement accuracy and detection of time changes can be facilitated.
（２）平均化処理回数Ｎｏ 、Ｎ＋等は、上記例ではＮ
ｏがＮ１の整数倍として説明したが、必ずしも整数倍の
必要はない、但し、平均化処理を行うときその倍数を考
慮する必要はある。(2) The number of times of averaging processing, N+, etc. is N in the above example.
Although it has been explained that o is an integer multiple of N1, it does not necessarily have to be an integer multiple, but it is necessary to consider the multiple when performing averaging processing.
（３）判定レベルをＳｌ ＜３２と設定したが、Ｓｌ
＝３２とし、判定レベルを１つとすることもＴｉｌ能で
ある。この時、ケース３については、ΔＤ＝Ｓ＋ （
＝８２）として扱うことができる。また、ΔＤ＝Ｓ＋
をケース１あるいはケース２に含めて処理し、ケース３
の処理は省略することもできる。(3) Although the judgment level was set as Sl < 32,
= 32, and setting the determination level to one is also possible. At this time, for case 3, ΔD=S+ (
=82). Also, ΔD=S+
is included in case 1 or case 2, and case 3 is processed.
This process can also be omitted.
（４）尚、一般的にはＳｌ ：Ｋｏ 、Ｎ２ ’ｙＫ＋
（−Ｊゴ「「］７Ｎコー・Ｋｏ ）とすれば良いが、得
られた温度分布の使用目的によっては、Ｓ１≦８２で！
）る限りＳｌ ＞Ｋｏ 、Ｎ２ ＞Ｋｌの任意の値を設
定することも可能である−
尚、Ｓｌ＜Ｋｏ、又はＮ２ ＜Ｋｌとすると、平均化処
理回数に依存する誤差分内となり、ケース１〜ケース３
の繰り返しに不都合が生じる場合があるので、その選定
は慎重に行わなければならない。(4) In general, Sl:Ko, N2'yK+
(-Jgo ""]7Nko・Ko ) However, depending on the purpose of use of the obtained temperature distribution, S1≦82!
), it is also possible to set arbitrary values for Sl > Ko and N2 > Kl - Note that if Sl < Ko or N2 < Kl, the error will be within the error depending on the number of averaging processes, and Case 1 ~Case 3
The selection must be made carefully, as repeating may cause inconvenience.
（５）ＯＴＤＲ装置１の入力としては、ラマン散乱光（
複数）、レーレ散乱光のいずれか、あるいはそれらの複
数を用いることができ、また、入力光あるいは入力光レ
ベル間の演算そのものをデータとするか、それぞれ温度
に換算した後のものをデータとすることや、０ＴＤＲ装
置１あるいは平均化処理選択回路２又は温度分布演算出
力回路３を適宜複数個用いること等の点にも限界がなく
、本発明の精神の範囲内で任意に変えることができる。(5) As input to the OTDR device 1, Raman scattered light (
(multiple), Lehle scattered light, or a plurality of them can be used, and the input light or the calculation between the input light levels itself can be used as data, or the data after each has been converted into temperature. There is also no limit to the use of a plurality of 0TDR devices 1, averaging processing selection circuits 2, or temperature distribution calculation output circuits 3 as appropriate, and any changes can be made within the scope of the spirit of the present invention.
尚、本発明は後方散乱光を用いた分布形温度センサに関
するものとして記述したが、その他、歪。Although the present invention has been described as relating to a distributed temperature sensor using backscattered light, other distortions may occur.
応力２曲げ、電磁界等、サンプリングと平均化処理によ
り、場所的に、時間的に変化する対象量を測定すること
に応用できることは明らかである。It is clear that the present invention can be applied to measuring target quantities that vary in location and time, such as stress 2 bending, electromagnetic field, etc., by sampling and averaging processing.
次に、本発明の具体例を第３図の試作品を用いて説明す
る。Next, a specific example of the present invention will be explained using a prototype shown in FIG.
第３図において、０ＴＤＲ装置１の中味は、概略、パル
スレーザ発光源１１、光合分波器（或いは光分岐器）１
２．１３、ストークス光とアンテイストークス光分離用
フィルタ１４、受光器１５、増幅・平均化処理回路１６
から成っており、ストークス光、アンチストークス光に
ついての演算をなす温度分布演算回路５が接続される。In FIG. 3, the contents of the 0TDR device 1 are roughly as follows: a pulsed laser emission source 11, an optical multiplexer/demultiplexer (or optical splitter) 1
2.13, Stokes light and Antei Stokes light separation filter 14, light receiver 15, amplification/averaging processing circuit 16
It is connected to a temperature distribution calculation circuit 5 that performs calculations on Stokes light and anti-Stokes light.
尚、発光パルスとサンプリングのタイミングを整合する
ために、増幅・平均化処理回路１６とパルスレーザ発光
源１１間も結ばれている。Incidentally, in order to match the timing of the light emission pulse and the sampling, the amplification/averaging processing circuit 16 and the pulsed laser light emission source 11 are also connected.
今回の試作では、０ＴＤＲ装置１による平均化早埋凹数
Ｎ１を１０５回とした。距離の分解能１ｍを得るため、
光フアイバセンサ４中の光速２００ｍ／′μｓに基づい
て、サンプリング間隔をＩｏｎｓとした。また発光パル
ス間隔を５０μｓ、センサ長を２０００ｍとした。In this prototype, the average number N1 of early burial recesses by the 0TDR device 1 was set to 105. To obtain a distance resolution of 1m,
Based on the speed of light in the optical fiber sensor 4 of 200 m/'μs, the sampling interval was set to Ions. Further, the emission pulse interval was set to 50 μs, and the sensor length was set to 2000 m.
このような装置により、温度−２０℃から１５０℃の範
囲で時間的に変化しない状態で実測したところ、測定温
度の精度は±３．４℃であることが確認できた。即ち、
５０μｓ Ｘ １０’回＝５秒毎に、２０００点の温度
が±３．４℃の精度で測定されたことになる。When such an apparatus was used to actually measure the temperature in the range of -20°C to 150°C without changing over time, it was confirmed that the accuracy of the measured temperature was ±3.4°C. That is,
This means that the temperature at 2000 points was measured every 5 seconds (50 μs x 10' times) with an accuracy of ±3.4°C.
一方、平均化処理選択回路２の中味は、主データ群と指
定ポイント等の記憶装置２１、データ比軸器２２を主体
としており、今回の検討ではＮｏ ＝１．５ ｘｔＯ’
回、Ｓ＋＝１℃、５２＝４℃と設定した。On the other hand, the contents of the averaging process selection circuit 2 mainly include a storage device 21 for the main data group and designated points, etc., and a data ratio scaler 22. In this study, No = 1.5 xtO'
times, S+=1°C, and 52=4°C.
前述の性能の０ＴＤＲ装置１と温度分布演算回路５に平
均化処理選択回路２を接続し、時間的に変化しない状態
での温度分布を測定したところ、その精度は約±０．９
°Ｃであることが確認できた。When the averaging processing selection circuit 2 was connected to the 0TDR device 1 and the temperature distribution calculation circuit 5 having the performance described above, and the temperature distribution was measured in a state where it did not change over time, the accuracy was approximately ±0.9.
It was confirmed that the temperature was °C.
このときの平均化処理回数は自動的に１．５ ｘ１０’
が選択された。The number of times of averaging processing at this time is automatically 1.5 x 10'
was selected.
即ち、７５秒毎に２０００点の温度が±０，９℃の精度
で測定されたことになる。That is, the temperature at 2000 points was measured every 75 seconds with an accuracy of ±0.9°C.
次に、２０００ｍの光フアイバセンサ４の一部をヒータ
上に密着させておき、その温度を第４図のように変化さ
せた。このとき０ＴＤＲ装置１の平均化処理回数を一定
値１０５回（手法■）及び１０′回（手法■）とし、第
３図の平均化処理選択回路２を使用せずに測定した場合
のその部分の測定結果を、それぞれ第４図にＸ印及び・
印で示した。そして、本発明による平均化処理選択回路
を使用したとき（手法■）の測定結果をＯ印で示した。Next, a part of the 2000 m optical fiber sensor 4 was placed in close contact with the heater, and its temperature was varied as shown in FIG. At this time, the number of times of averaging processing of the 0TDR device 1 is set to a constant value of 105 times (method ■) and 10' times (method ■), and the part is measured without using the averaging processing selection circuit 2 in Fig. 3. The measurement results are marked with an X and
Indicated with a mark. The measurement results obtained when the averaging process selection circuit according to the present invention was used (method ①) are indicated by an O mark.
尚、この場合温度変化の激しい時間帯は５秒毎のデータ
が、温度変化の小さい時間帯は７５秒間の平均処理のデ
ータが出力されることになる。In this case, data for every 5 seconds is output during periods of rapid temperature change, and data averaged over 75 seconds is output for periods of small temperature change.
その間は直前のデータがそのまま成り立つと見なせる。During that time, it can be assumed that the previous data holds as is.
このようにして得られた上記３つの測定手法ｌ、■、■
によるＡＢＣＤ各時点（第４図）での温度を、ヒータの
温度（真値）と比較して、第５図に示す、定常時のＡ、
Ｄ点では手法■、■の精度が良好で、手法Ｉでは最大３
．１℃の誤差が生じ、変化時のＢ点あるいは０点では手
法■の誤差が７．６°Ｃにも達するのに対し、千法工で
は２．１℃、手法■では２．２°Ｃに納まっており、総
合的に手法■の秀れていることが確認できた。The above three measurement methods obtained in this way l, ■, ■
By comparing the temperature at each point in ABCD (Fig. 4) with the temperature (true value) of the heater, the temperature A at steady state shown in Fig. 5,
At point D, the accuracy of methods ■ and ■ is good, and the accuracy of method I is up to 3.
．． An error of 1°C occurs, and at point B or 0 at the time of change, the error for method ■ reaches 7.6°C, while for Senboko it is 2.1°C, and for method ■ it is 2.2°C. It was confirmed that method ■ is superior overall.
［発明の効果］
以上のように本発明によれば、平均化処理選択手段によ
って、データ群Ｄ１とその直前に得ている主データ群Ｄ
Ｏとの差分ΔＤの判定レベルに対する大小関係より、物
理量の変化が時間的に綬やかな状悪か、或いは時間変化
が急激な状態にあるかが判定され、それぞれに応じた適
切な主データ群に基づいて測定が行われる。このため、
従来の後方散乱光検出による測定の精度と時間変化の把
握という矛盾が解決され、時間的、場所的に変化する対
象物の温度を、その状況に応じて精度よく測定すること
ができる。[Effects of the Invention] As described above, according to the present invention, the averaging process selection means selects the data group D1 and the main data group D obtained immediately before it.
Based on the magnitude relationship of the difference ΔD with O with respect to the determination level, it is determined whether the change in the physical quantity is irregular over time, or whether the change over time is rapid, and an appropriate main data group is selected according to each. Measurements are taken based on. For this reason,
The contradiction between the accuracy of measurement using conventional backscattered light detection and the understanding of changes over time is resolved, and the temperature of an object, which changes over time and location, can be measured with high accuracy depending on the situation.
−本の光ファイバで、それに沿った多点の物理量が一度
に測定できる分布形光ファイバセンシングシステムは、
それ自体従来にない工業的、経済的効果を生み出すこと
は明らかである。しかし、現実の工業分野では定常状態
のみならず過度変化状態の把握、あるいは突発的な異常
状態の把握も併せて、極力精度よく行うことが望まれる
。この点に関し本発明は極めて大きな効果を発揮し、分
布形光ファイバセンサの適用分野を拡げ、多くの産業分
野に適合し得る高性能のセンシングシステムの構築を可
能にする。-A distributed optical fiber sensing system that can measure physical quantities at multiple points along a single optical fiber at once.
It is clear that this in itself will produce unprecedented industrial and economic effects. However, in the actual industrial field, it is desirable to grasp not only steady states but also transient states, or sudden abnormal states, with as much precision as possible. In this regard, the present invention exhibits an extremely large effect, expands the field of application of distributed optical fiber sensors, and makes it possible to construct a high-performance sensing system that can be adapted to many industrial fields.
第１図は本発明の基本構成図、第２図は本発明の方法に
おける主要部の平均化処理選択回路の処理手順を示す図
、第３図は本発明に基づき試作した装置の構成図、第４
図はその装置を用いて得られた高精度の温度変化測定結
果を示す図、また第５図はその具体的数値データを示し
た図、第６図は従来のセンシングシステムの動作説明図
である。
図中、１は０ＴＤＲ装置、２は平均化処理選択回路、３
は温度演算・表示装置、４はセンサ光ファイバ、５は温
度分布演算装置、１１はパルスレーザ発光源、１２．１
３は光合分波器或いは光分岐器、１４は光学フィルタ、
１５は受光器、１６は増幅・サンプリング・平均化処理
装置を示す。FIG. 1 is a basic configuration diagram of the present invention, FIG. 2 is a diagram showing the processing procedure of the averaging process selection circuit, which is the main part of the method of the present invention, and FIG. 3 is a configuration diagram of a device prototyped based on the present invention. Fourth
The figure shows the highly accurate temperature change measurement results obtained using the device, Figure 5 shows the specific numerical data, and Figure 6 is an explanatory diagram of the operation of the conventional sensing system. . In the figure, 1 is the 0TDR device, 2 is the averaging process selection circuit, and 3
1 is a temperature calculation/display device, 4 is a sensor optical fiber, 5 is a temperature distribution calculation device, 11 is a pulsed laser light source, 12.1
3 is an optical multiplexer/demultiplexer or optical splitter; 14 is an optical filter;
15 is a light receiver, and 16 is an amplification/sampling/average processing device.
Claims (1)
後方散乱光を利用して測定するセンシングシステムにお
いて、パルス入射光に対する後方散乱光をサンプリング
し、同一サンプリングタイミングに相当するデータを逐
次多数回平均化処理するに当って、一連のサンプリング
と物理量の変化が時間的に緩やかな状態での測定精度が
高い平均化処理回数Ｎ＿０よりも回数が少ない平均化処
理回数Ｎ＿１とを行って得たデータ群Ｄ＿１を平均化処
理選択手段に導き、該手段において、このデータ群 Ｄ＿１をその直前に得ている主データ群Ｄ＿０とを対応
するデータ毎に対比して差分ΔＤを作成し、その差分Δ
Ｄを判定レベルと比較し、差分ΔＤと判定レベルよりの
大小関係により、物理量の変化が時間的に緩やかな状態
のときは、上記Ｎ＿１回処理のデータ群Ｄ＿１とその直
前に得ている主データ群Ｄ＿０の回数の重味を考慮した
平均化処理により新たなデータ群を得て、これを新たな
主データ群として測定を行い、物理量の時間変化が急激
な状態のときは、上記Ｎ＿１回処理のデータ群Ｄ＿１を
新たな主データ群として測定を行うことを特徴とする分
布形光ファイバセンサのセンシング方法。 ２、判定レベルをＳ＿１、Ｓ＿２（Ｓ＿１＜Ｓ＿２）の
２種とし、Ｎ＿１回処理のデータ群Ｄ＿１とその直前に
得ている主データ群Ｄ＿０とを対比して得た上記差分Δ
Ｄを判定レベルＳ＿１、Ｓ＿２と比較し、 ΔＤ＜Ｓ＿１のときには、Ｎ＿１回処理のデータ群Ｄ１
とその直前に得ている主データ群の回数の重味を考慮し
た平均化処理により新たなデータ群を得て、これを新た
な主データ群とし、物理量の変化が時間的により緩やか
な状態での高精度測定を行い、 またΔＤ＞Ｓ＿２のときは、Ｎ＿１回処理のデータ群を
新たな主データ群として、物理量の時間変化がより急激
な状態での高精度測定を行い、 更にＳ＿１≦ΔＤ≦Ｓ＿２のときには、その継続性によ
り上記いずれかの処理により、新たなデータ群を得て、
物理量の時間変化が中間的な状態での高精度測定を行う
ことを特徴とする請求項１記載の分布形光ファイバセン
サのセンシング方法。 ３、定常状態での理論上の測定誤差として、Ｎ＿１回処
理時の誤差Ｋ＿１とＮ＿０回処理時の誤差Ｋ＿０との間
に、Ｋ＿１∝√（Ｎ＿０／Ｎ＿１）・Ｋ＿０が成り立つ
関係において、上記判断レベルをＳ＿１＝Ｋ＿０、Ｓ＿
２＝Ｋ＿１と設定することを特徴とする請求項２記載の
分布形光ファイバセンサのセンシング方法。[Claims] 1. In a sensing system that uses backscattered light of an optical fiber to measure physical quantities that change depending on position and time, backscattered light with respect to pulsed incident light is sampled and data corresponding to the same sampling timing is obtained. In sequentially performing averaging processing many times, a series of samplings and an averaging processing number N_1, which is smaller than the averaging processing number N_0, which has high measurement accuracy in a state where physical quantities change gradually over time, are performed. The data group D_1 obtained by the method is led to an averaging processing selection means, and in the means, this data group D_1 is compared with the main data group D_0 obtained immediately before it for each corresponding data to create a difference ΔD, The difference Δ
D is compared with the judgment level, and if the change in the physical quantity is gradual over time, the difference ΔD is larger than the judgment level, and if the change in the physical quantity is gradual over time, the data group D_1 processed N_1 times and the main data obtained immediately before it are A new data group is obtained through averaging processing that takes into account the importance of the number of times of group D_0, and this is measured as a new main data group, and when the physical quantity changes rapidly over time, the above N_1 processing is performed. A sensing method for a distributed optical fiber sensor, characterized in that measurement is performed using a data group D_1 as a new main data group. 2. The difference Δ obtained by comparing the data group D_1 processed N_1 times with the main data group D_0 obtained immediately before using two determination levels, S_1 and S_2 (S_1<S_2).
Compare D with judgment levels S_1 and S_2, and when ΔD<S_1, data group D1 processed N_1 times.
A new data group is obtained by averaging processing that takes into consideration the weight of the number of times the main data group obtained immediately before that, and this is used as a new main data group, and the changes in physical quantities are more gradual over time. Furthermore, when ΔD>S_2, the data group processed N_1 times is used as a new main data group, and high-precision measurement is performed in a state where the time change of the physical quantity is more rapid. Furthermore, when S_1≦ΔD When ≦S_2, a new data group is obtained by any of the above processes due to its continuity,
2. The sensing method for a distributed optical fiber sensor according to claim 1, wherein highly accurate measurement is performed in a state where the physical quantity changes over time in an intermediate state. 3. The above judgment is made based on the relationship that K_1∝√(N_0/N_1)・K_0 holds between the error K_1 when processing N_1 times and the error K_0 when processing N_0 times as a theoretical measurement error in a steady state. Set the level to S_1=K_0, S_
3. The sensing method for a distributed optical fiber sensor according to claim 2, wherein 2=K_1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP1019996A JPH0786436B2 (en) | 1989-01-30 | 1989-01-30 | Sensing method of distributed optical fiber sensor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP1019996A JPH0786436B2 (en) | 1989-01-30 | 1989-01-30 | Sensing method of distributed optical fiber sensor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH02201133A true JPH02201133A (en) | 1990-08-09 |
| JPH0786436B2 JPH0786436B2 (en) | 1995-09-20 |
Family
ID=12014771
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP1019996A Expired - Fee Related JPH0786436B2 (en) | 1989-01-30 | 1989-01-30 | Sensing method of distributed optical fiber sensor |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH0786436B2 (en) |
Cited By (4)
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|---|---|---|---|---|
| JP2016151505A (en) * | 2015-02-18 | 2016-08-22 | 富士通株式会社 | Temperature measurement system, temperature measurement method and program |
| US9816878B2 (en) | 2012-10-26 | 2017-11-14 | Fujitsu Limited | Temperature measurement system and abnormality detection method |
| JP2019090696A (en) * | 2017-11-15 | 2019-06-13 | 横河電機株式会社 | Temperature measuring system and temperature measuring method |
| US10712211B2 (en) | 2015-05-13 | 2020-07-14 | Fujitsu Limited | Temperature measurement device, temperature measurement method, and computer-readable non-transitory medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS5815159A (en) * | 1981-07-21 | 1983-01-28 | Fanuc Ltd | Digital speed detecting system |
| JPS61270632A (en) * | 1985-05-25 | 1986-11-29 | Hitachi Cable Ltd | Optical fiber type measuring instrument for temperature distribution |
| JPS62110160A (en) * | 1985-08-20 | 1987-05-21 | ヨ−ク・リミテツド | Optical time-region reflection measurement |
-
1989
- 1989-01-30 JP JP1019996A patent/JPH0786436B2/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS5815159A (en) * | 1981-07-21 | 1983-01-28 | Fanuc Ltd | Digital speed detecting system |
| JPS61270632A (en) * | 1985-05-25 | 1986-11-29 | Hitachi Cable Ltd | Optical fiber type measuring instrument for temperature distribution |
| JPS62110160A (en) * | 1985-08-20 | 1987-05-21 | ヨ−ク・リミテツド | Optical time-region reflection measurement |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9816878B2 (en) | 2012-10-26 | 2017-11-14 | Fujitsu Limited | Temperature measurement system and abnormality detection method |
| JP2016151505A (en) * | 2015-02-18 | 2016-08-22 | 富士通株式会社 | Temperature measurement system, temperature measurement method and program |
| US10267689B2 (en) | 2015-02-18 | 2019-04-23 | Fujitsu Limited | Temperature measuring system and temperature measuring method |
| US10712211B2 (en) | 2015-05-13 | 2020-07-14 | Fujitsu Limited | Temperature measurement device, temperature measurement method, and computer-readable non-transitory medium |
| JP2019090696A (en) * | 2017-11-15 | 2019-06-13 | 横河電機株式会社 | Temperature measuring system and temperature measuring method |
Also Published As
| Publication number | Publication date |
|---|---|
| JPH0786436B2 (en) | 1995-09-20 |
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