JPH02108967A - Detecting device for abnormality of water quality - Google Patents
Detecting device for abnormality of water qualityInfo
- Publication number
- JPH02108967A JPH02108967A JP63261457A JP26145788A JPH02108967A JP H02108967 A JPH02108967 A JP H02108967A JP 63261457 A JP63261457 A JP 63261457A JP 26145788 A JP26145788 A JP 26145788A JP H02108967 A JPH02108967 A JP H02108967A
- Authority
- JP
- Japan
- Prior art keywords
- data
- abnormality
- water quality
- value
- time
- 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
- 230000005856 abnormality Effects 0.000 title claims abstract description 66
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 39
- 238000001514 detection method Methods 0.000 claims abstract description 31
- 230000015654 memory Effects 0.000 abstract description 39
- 230000010354 integration Effects 0.000 abstract description 17
- 238000013500 data storage Methods 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 abstract description 4
- 238000011156 evaluation Methods 0.000 description 12
- 230000002159 abnormal effect Effects 0.000 description 7
- 238000000034 method Methods 0.000 description 7
- 238000011157 data evaluation Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000003936 working memory Effects 0.000 description 3
- OAKJQQAXSVQMHS-UHFFFAOYSA-N Hydrazine Chemical compound NN OAKJQQAXSVQMHS-UHFFFAOYSA-N 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000004069 differentiation Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 206010065954 Stubbornness Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 229910052708 sodium Inorganic materials 0.000 description 1
- 239000011734 sodium Substances 0.000 description 1
- 230000002618 waking effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E30/00—Energy generation of nuclear origin
- Y02E30/30—Nuclear fission reactors
Landscapes
- Monitoring And Testing Of Nuclear Reactors (AREA)
Abstract
Description
【発明の詳細な説明】
〔産業上の利用分野〕
本発明は原子力発電所などの水質管種システムに使用さ
れる水質異常検出装置に関する。DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a water quality abnormality detection device used in water quality pipe systems such as nuclear power plants.
従来この個の水質異常検出装置の例として、第１、第２
の例があり、第１の例は、特定のしきい値を測定データ
が逸脱することによって水質異常の検出を行う方式と、
第２の例は、％許公開昭５２−１３７５９４にあるよう
に水質の変化率（微分）によって水質の収束値を演算し
、その得られた数値と特定のしきい値との比較により、
水質異常を検出するという方式がおる。As an example of conventional water quality abnormality detection devices, first and second
The first example is a method for detecting water quality abnormalities when measured data deviates from a specific threshold;
The second example is to calculate the convergence value of water quality based on the rate of change (differentiation) of water quality, as described in Japanese Patent Publication No. 52-137594, and compare the obtained value with a specific threshold.
There is a method to detect water quality abnormalities.
〔発明が解決しようとす９課題〕 前述の第１の方式にあっては次のような間趙点がある。[9 problems that the invention attempts to solve] In the first method described above, there are the following points.
＋１３ 特定のしきい埴逸脱を異常検出Ｋ 、ＩＪ用
する場合、異常が検出さｎるまでの時間遅れが生じる。+13 When a specific threshold deviation is used for abnormality detection K and IJ, a time delay occurs until an abnormality is detected.
（２） 測定データが上昇傾向にあるのか、下降傾向
にあるのか、又その程度はどれ位か、変動はしていない
かという状態把握ができなかったため、異常原因の同定
、対策の実行が遅扛る。(2) Because it was not possible to ascertain whether the measured data was trending upward or downward, to what degree, and whether there was any fluctuation, identification of the cause of the abnormality and implementation of countermeasures were delayed. to snatch
（３］ 異常検出を早めるためにしきい値を通常１１
１Ｉに近づけるとデータのもつノズルによって誤った検
出をしてしまう。(3) The threshold value is normally set to 11 to speed up abnormality detection.
If it approaches 1I, erroneous detection will occur due to the nozzle of the data.
またＮ述の第２０方式にあっては次のような問題点があ
る。すなわち、単に変化率（微分）のみによってるる時
間後の数値を演算する場合、水質が微妙に変動している
場合、変化率の符号が正負に頻繁に切り替わり水質の収
束値の演算は不可能であシ、異常の検出ができない。Furthermore, the 20th method with N statements has the following problems. In other words, when calculating a value after a period of time that depends solely on the rate of change (differentiation), if the water quality is slightly fluctuating, the sign of the rate of change will frequently switch between positive and negative, making it impossible to calculate a converged value for water quality. Unfortunately, abnormalities cannot be detected.
そこで、不発明は異′さ検出を早期に行え、検出された
異常の程度のに量化が行え、また異常の状態の把握がで
き゛、ノイズの検出による誤った異常検出を防止でさる
水質異常検出装置を提供することを目的とする。Therefore, the invention is able to detect abnormality at an early stage, quantify the degree of the detected abnormality, grasp the state of the abnormality, and prevent erroneous abnormality detection due to noise detection. The purpose is to provide a detection device.
〔課題を解決するための手段〕
本発明は前記目的を達成するため、
水質データを計器又は手分析により時系列的に入カレ、
定常値からの偏差が、水質データのノイズ幅以外の範囲
において積分するとともに、正側、負側そｎ（″ｎ独立
に積分を行い、その値を格納する第１の手段と、
この′ｉｊｇｌの手段により求めた倫理の積分値を予じ
め設定した設定値にもとづいて評価することにより異常
を検出する第２の手段と、
削記第１の手段により求めた偏差の積分値の時間平均、
時系列データの微分１直の積算により異常を定量化する
第３の手段と、
この第３の手段で′ｔＲ算されたデータから異常検出値
、通常積分値と異常検出されたいずｎかの符号の積分値
の比較により水質の状態を評価する第４の手段と、
この第４の手段で＃ｆＩＩｌｌｉさ扛た水質の状態を呈
示するｍ５の手段と、
を備えたものでるる。[Means for Solving the Problem] In order to achieve the above-mentioned object, the present invention includes inputting water quality data in time series using a meter or manual analysis,
The deviation from the steady value is integrated in a range other than the noise width of the water quality data, and the first means is to integrate independently on the positive side and the negative side, and to store the values; A second means for detecting an abnormality by evaluating the integral value of ethics obtained by the above method based on a preset value; and a time average of the integral value of the deviation obtained by the first method. ,
A third means of quantifying an abnormality by integrating the first differential of time series data, and calculating an abnormality detection value, a normal integral value, and an abnormality detected value from the data calculated by this third means. A fourth means for evaluating the state of water quality by comparing the integral values of signs, and a means for m5 for presenting the state of water quality expressed by the fourth means.
〔作用」
本発明は水質ｒ−夕を計器又は手分析により時系列的に
入力し定常値からの偏差を正側、負側独立に積分し、そ
れぞｎについて、あらかじめ設定した設定値にもとづい
て評価するので、僅かな水質データの変動をも見逃がす
ことなく異常を検出する。[Operation] The present invention inputs the water quality r-t in a time-series manner using a meter or manual analysis, integrates the deviation from the steady value independently on the positive side and the negative side, and calculates the value for each n based on the preset setting value. Since the water quality data is evaluated based on the water quality data, abnormalities can be detected without overlooking even the slightest fluctuations in water quality data.
また、本発明は水質データのノイズ幅以外の範囲におい
て、前記偏差を積分することによって誤った異常検出を
防止できる。Furthermore, the present invention can prevent erroneous abnormality detection by integrating the deviation in a range other than the noise width of water quality data.
さらに本発明は積分開始から、異常検出までの期間の先
の値分値の時間平均、時系列データの微分値の積算等を
行うことによって、異常の程度を定量化できる。Furthermore, the present invention can quantify the degree of anomaly by performing time averaging of values over a period from the start of integration until detection of an anomaly, integration of differential values of time series data, and the like.
また本発明は異常検出時、異常積分値と異常検出さｎた
いずｎかの符号の積分値との比較を行うことにより、正
側（負側ン積分値と通′に積分値とが同等である場合上
昇（下降）傾向にある、あるいは正側（側１槓分値が通
常積分値よシ十分大きい場合、変動しているといった＃
ｆ１１ＩＩＪが何える。Furthermore, when an abnormality is detected, the present invention compares the abnormality integral value with the integral value of any sign of the detected abnormality, so that the positive side (negative side integral value and the integral value are generally equal). If , there is an upward (downward) trend, or if the value on the positive side (one side) is sufficiently larger than the normal integral value, it is said to be fluctuating.
What does f11IIJ say?
不発明は１４常検出時、その時刻、異常データ名、異′
にの程度、異常の状態を呈示することができるので、す
みやかに善後策が施せる。14 When non-invention is detected, the time, abnormal data name, abnormality'
Since it is possible to indicate the degree of abnormality and the state of the abnormality, corrective measures can be taken promptly.
第１図に本発明の実施例を示す。 FIG. 1 shows an embodiment of the present invention.
本、水質異常検出装置は、装置全体の制御機構１、グラ
ンドの計器や手分析による後述する水質データを入力す
る水質データ入力機？４２、入力されたｒ−夕を格納す
る時系列データ格納メモリ３、定常値を格納する定常値
格納メモリ４、測定データのノイズ幅を格納する測定ｒ
−タノイズ幅格納メモリ５、人力されたデータと通常値
との偏差のノイズ幅を超える領域について通常積分、正
側積分および負１１４１積分を行う偏差積分機構６、お
よび七ｎらの結果を格納する通常積分値格納メモリ７、
正１１４ｉｌ＆分格納メモリ８、負側積分格納メモリ９
、偏差積分機構６により得られた積分値を後述するよ５
に評価し異常検出並びに状態把握を行うデータ評価機構
１０、評価のための数１（Ｉ！を格納するデータ評価用
数値表格納メモリ１）、評価結果を格納する評価結果格
納メモリＪ２、異常の７ｍＫｔ一定量化する異？ｉＴ程
夏定菫化機＃Ｉｔ１３、定量化ぢれた数値を格納する正
側異常定量値格納メモリ１４、負側異常定量値格納メモ
リ１５、旺価結釆を表示するｉｆｆ価結果表示機構１６
、他の計算機との通信機構１７、これらの作業のための
ワーキングメモリ１８、および各機構を連絡するデータ
入力機９よシなる。This water quality abnormality detection device consists of a control mechanism 1 for the entire device, a water quality data input device that inputs water quality data from ground meters and manual analysis, which will be described later. 42, time series data storage memory 3 for storing input r-events, steady value storage memory 4 for storing steady values, measurement r for storing noise width of measurement data
- a noise width storage memory 5, a deviation integration mechanism 6 that performs normal integration, positive side integration, and negative 1141 integration for a region exceeding the noise width of the deviation between the manually input data and the normal value, and stores the results of Nana et al. Normal integral value storage memory 7,
Positive 114il & minute storage memory 8, negative integral storage memory 9
, the integral value obtained by the deviation integration mechanism 6 will be described later.
a data evaluation mechanism 10 that evaluates and detects anomalies and grasps the state; a data evaluation numerical table storage memory 1 that stores the number 1 (I!) for evaluation; an evaluation result storage memory J2 that stores evaluation results; 7mKt constant quantity difference? iT Chengxia Ding sumification machine #It13, positive side abnormal quantitative value storage memory 14 for storing quantified values, negative side abnormal quantitative value storage memory 15, if value result display mechanism 16 for displaying the desired value conclusion.
, a communication mechanism 17 with other computers, a working memory 18 for these operations, and a data input machine 9 that communicates with each mechanism.
ここで、水ｆ（７″−夕とはｐｆ（、電気伝導率、溶存
ｒＲ素！Ｉ度、ヒドラジ／濃度、ナトリウム濃度などの
水質を表わす値のことをさしている。また、前述の積分
値の評価は、あらかじめ設定した値との比較による異′
ｇの慣用か、積分値の符号により上昇、下降を区別した
シ、あるいは積分値の大きさにより異常の大きさを区別
する。Here, water f(7''-1) refers to values representing water quality such as pf(, electrical conductivity, dissolved rR element, I degree, hydrazine/concentration, and sodium concentration. Also, the above-mentioned integral value The evaluation of
The conventional use of g is used to distinguish between rise and fall based on the sign of the integral value, or the magnitude of the abnormality is distinguished based on the magnitude of the integral value.
本装置はユーデの指令により起動され以下の処理を繰シ
返し実行する。起ｌＩｈ後、制＃機慴１０指令によりま
ず水質データ入力機構２が動作を始め、計器るるいは手
分析データを入力処櫨する。処理されたデータは時系列
データ格納メモリ３へ誉き込まれると同時に偏差積分機
構６へ送られる。ｒ−夕を受けた偏差積分機構６は、定
′Ｉｖ値格網メモリ４に格納された定常値および測定デ
ータノイズ幅格納メモリ５に格納されたノイズ幅を参照
し偏差を算出したのち、その偏差の時間積分を行う、こ
の時間積分は、通常積分と正側、負側そｎぞれ独立に行
う積分とにわけらｎ、積分結果はそｎぞｎ通常積分値格
納メモリ７、正ＩＪ４積分値格納メモリ８、および負側
積分値格納メモリ９に誉き込まれる。正側積分値および
負１１ｔｌＩ積分値は、データ評価機構ＪＯに送らｎｙ
’−夕を受けたデータ評価機４〕は、データ評価用数値
表格納メモリ１ノに格納さｎているデータ評価月数１直
衣を参照し、異常の検出及び異常状態の抱擁を行い、そ
の結果を評価結果格納メモリ１２に誉き込む。This device is activated by Yude's command and repeatedly executes the following process. After waking up, the water quality data input mechanism 2 starts operating according to the control command 10, and inputs the data from the instrument or by manual analysis. The processed data is stored in the time series data storage memory 3 and simultaneously sent to the deviation integration mechanism 6. The deviation integration mechanism 6 that receives the r-event calculates the deviation by referring to the steady value stored in the constant value network memory 4 and the noise width stored in the measured data noise width storage memory 5. This time integration, which performs time integration of the deviation, is divided into normal integration and integration performed independently on the positive side and negative side.The integration results are stored separately in the normal integral value storage memory 7, positive IJ4. It is stored in the integral value storage memory 8 and the negative side integral value storage memory 9. The positive integral value and the negative 11tlI integral value are sent to the data evaluation mechanism JO.
The data evaluation machine 4 that received the data refers to the data evaluation month number 1 stored in the data evaluation numeric table storage memory 1, detects abnormalities and embraces abnormal conditions, and The results are stored in the evaluation result storage memory 12.
一方、異常が検出され扛ば異常＃ｉ度廻を化機構１３は
、時系列データメモリ３、谷積分１直格稍メモ’）７ｅ
８＊９及び評価結果格納メモリ１２に格納されたデータ
を参照し、時系列データの微分値の積算、積分値の時間
平均を行いｓｇ＠度の足置化を行う。定量化さｎたｒ−
夕は、＃ｆ価紹釆格網メモリ１２に他のｒ−夕と対で誉
さ込まれる。On the other hand, if an abnormality is detected, the abnormality #i degree rotation mechanism 13 will have a time series data memory 3, a valley integral 1 direct case memo') 7e
8*9 and the data stored in the evaluation result storage memory 12, the differential values of the time series data are integrated, the integral values are averaged over time, and sg@degrees are calculated. quantified nta r-
The #f price is stored in the network memory 12 in pairs with other r-times.
一連の処理が終われば、評価結果表示機構１６は、Ｆｌ
！価結米格納メモリ１２の内容を参照し異常が−い場合
はその旨を、異常がある場合は、異常データ名、異常の
程度、および異常の状態の表示を行う。When the series of processing is completed, the evaluation result display mechanism 16 displays Fl.
! The contents of the rice storage memory 12 are referred to, and if there is an abnormality, that fact is displayed, and if there is an abnormality, the name of the abnormal data, the degree of the abnormality, and the state of the abnormality are displayed.
他の計１４機との通１ｇ機構は評価Ｍ来の転送、プラン
トデータの受信等を行う。個々の慎溝は必要な数値全１
面々の格納メモリからワーキングメモリ１８にデータを
ロードし、処理を行う、行った結果は必要に応じた格納
メモリへの誉き込みが行なわれる。データ、１１Ｂ号の
送・受信は共通のｒ−タパス１９を用いて行なわｎる。The 1g mechanism, which communicates with a total of 14 other machines, transfers evaluation M data, receives plant data, etc. For each Shinzo, the required number is 1
Data is loaded from each storage memory into the working memory 18 and processed, and the results are stored in the storage memory as necessary. Transmission and reception of data and No. 11B is performed using a common r-tapass 19.
次に、異常検出の状況を第２図を用いて説明する。第２
図（ｉｌ）には水翼時系列データ（測定値）が上昇傾向
を示している例１（実りと、変動を示している例２（破
線〕を具体的に示したものである。第２図（ｂ）は第２
図（りの１ｌｉｌｌＪ定イ直を積分し九偏差積分値の時
系列データを示すもので、ノイズ幅を超えた時点で積分
が開始さｎ、積分値も時系列に求められていく。ノイズ
幅は、過大の夾槓による定常値に対し個々のデータのも
つ特性によって与えられる。積分１直が積分値評価用し
きい値Ｘを逸脱すれば、異常（異常検出点ｌ）と見なす
。第２図（ｂ）における例１の上昇傾向の場合は、正側
積分値と通常積分値が同等である。また例２の変動の場
合、例２（ｌ）に示すものは正側積分値で６９、例２（
３）に示すものは負側積分値はデータが上下のノイズ幅
を逸脱した場合に増加していくため、ステ、プ状に変化
する。どちらかの積分値かが積分値評価用しきい値Ｘを
逸脱した場合に異常（異常検出点２）と見なす。この特
例２１２）に示すように通常積分値は増減をくり返すた
め正−積分値又は負側積分値との比較において十分な差
があシ、入力の測定データが変動していると判断できる
。こｎに対し、前述した従来同では、通常値よシかけは
なれ丸しきい値（第２図の従来のしきい値）を時系列デ
ータが逸脱することによっての与異常（異常検出点３）
と見なしており、異′ｌＩｆ＃ｌ出が本発明の実施例の
異常検出点ｌ、異常愼ＩＪ：１点２ンよシ遅く、異常程
度の定着化、異常状態の把握ができなかりた。Next, the situation of abnormality detection will be explained using FIG. 2. Second
Figure (il) specifically shows Example 1 (fruitfulness) where the waterfoil time series data (measured values) show an upward trend, and Example 2 (dashed line) where the waterfoil time series data (measured values) show fluctuations. Figure (b) is the second
The figure shows the time series data of the nine deviation integral value obtained by integrating the 1lillJ constant straight line.The integration starts when the noise width is exceeded, and the integral value is also found in time series.Noise width is given by the characteristics of individual data with respect to the steady value due to excessive contamination.If the integral 1 deviates from the integral value evaluation threshold X, it is regarded as an abnormality (abnormality detection point l).Second In the case of the upward trend in Example 1 in Figure (b), the positive integral value and the normal integral value are equivalent.In addition, in the case of the fluctuation in Example 2, the positive integral value shown in Example 2 (l) is 69 , Example 2 (
In the case shown in 3), the negative integral value increases when the data deviates from the upper and lower noise widths, so it changes in a step-like manner. If any of the integral values deviates from the integral value evaluation threshold X, it is regarded as abnormal (abnormality detection point 2). As shown in this special case 212), since the integral value normally increases and decreases repeatedly, there is a sufficient difference in comparison with the positive integral value or the negative integral value, and it can be determined that the input measurement data is fluctuating. On the other hand, in the conventional method described above, an abnormality (abnormality detection point 3) caused by the time series data deviating from the normal value and the separate circle threshold (the conventional threshold in Fig. 2) is detected.
It is assumed that the abnormality If#l output is slower than the abnormality detection point l and abnormality detection point IJ: 1 point 2 of the embodiment of the present invention, and the degree of abnormality becomes established and the abnormal state cannot be grasped. .
第３図は本発明の水質異常検出装置ｉ１を使用して冥１
１ｉｃＪ！！常検出を行ったときの実験結果を示す図で
あシ、第４図は第３図をもとに電気伝導率と時間の関係
を示す図である。これらの図は、０．１時間（Ｈｒ）母
の水質データのうちの電気伝導率について正側すなわち
４．０５μＶ−以上（定常値４．００に対しノイズ０．
０５を考慮した数値ンの積分値を表わしたものである。Figure 3 shows how the water quality abnormality detection device i1 of the present invention is used.
1icJ! ! 4 is a diagram showing the experimental results when regular detection is performed, and FIG. 4 is a diagram showing the relationship between electrical conductivity and time based on FIG. 3. These figures show that the electrical conductivity of the water quality data for 0.1 hour (Hr) is on the positive side, that is, 4.05 μV- or more (noise is 0.00 compared to the steady value of 4.00).
This represents the integral value of numerical values considering 05.
両図から明らかなように正側積分１１に累、ｔｉ（＃ｆ
価値）が０．０１７ μｓ ・Ｈｒを超えた時点で異常
と刊断じ、簀＠ｉを発するものとなっている。なお、従
来装置の異常検出しきい値は４．２となっていた。As is clear from both figures, in the positive integral 11, ti(#f
When the value (value) exceeds 0.017 μs/Hr, it is judged as an abnormality and a signal @i is issued. Note that the abnormality detection threshold of the conventional device was 4.2.
〔発明の効果」
本発明によれば異常検出を早期に行え、検出さｎｆｃ典
密の程度の定量化が行え、また異常の状態すなわち上昇
順向、下降頑同あるいは変動といった把握が行え、さら
にノイズの検出による誤った異常検出を防止できる水質
異常検出装置ｔを提供できる。[Effects of the Invention] According to the present invention, anomalies can be detected early, the degree of detected NFC integrity can be quantified, and the state of the anomaly, that is, upward trend, downward stubbornness, or fluctuation, can be understood. It is possible to provide a water quality abnormality detection device t that can prevent erroneous abnormality detection due to noise detection.
第１図は本発明の水質異常検出装置の一実施例の構成を
示すプロ、り図、第２図は本発明における異常検出例を
説明するための図、第３図および第４図は本’Ａｋ３Ｆ
’ｒＫよる異常検出例の具体例を示す図である。
ｌ・・・制御ａ！構、２・・・水質ｒ−タ入力４１１ｅ
構、３・・・時系列データ格納メモリ、４・・・定常値
格納メモリ、５・・・測定データ幅格納メモリ、６・・
・偏差積分機構、７・・・通常積分値格納メモリ、８・
・・正−積分値格納メモリ、９・・・負＄１１Ｉ積分値
格納メモリ、１０・・・データ計画機構、１）・・・デ
ータｄ画用数値表格納メモリ、１２・・・評価結果格納
メモリ、１３・・・異常程度足置化機構、１４・・・正
−異常足１値格納メモリ、１５・・・負側異常定ｆ＋ｌ
ｔ格納メモリ、１６・・・評価Ｎ未表示機構、１７・・
・他ｉｔ′ｉ′ｘ愼との通信機構、１８・・・ワーキン
グメモリ、２０・・・水質異常検出装置。Fig. 1 is a schematic diagram showing the configuration of an embodiment of the water quality abnormality detection device of the present invention, Fig. 2 is a diagram for explaining an example of abnormality detection in the present invention, and Figs. 'Ak3F
It is a figure which shows the specific example of the abnormality detection example by 'rK. l...control a! Structure, 2...Water quality data input 411e
Structure, 3... Time series data storage memory, 4... Steady value storage memory, 5... Measured data width storage memory, 6...
- Deviation integration mechanism, 7... Normal integral value storage memory, 8.
... Positive integral value storage memory, 9... Negative $11I integral value storage memory, 10... Data planning mechanism, 1)... Numerical table storage memory for data d drawing, 12... Evaluation result storage Memory, 13...Abnormality foot placement mechanism, 14...Positive-abnormal foot single value storage memory, 15...Negative side abnormality constant f+l
t storage memory, 16...Evaluation N non-display mechanism, 17...
・Communication mechanism with other IT'i'x machines, 18... working memory, 20... water quality abnormality detection device.
Claims (1)
定常値からの偏差が、水質データのノイズ幅以外の範囲
において積分するとともに、正側、負側それぞれ独立に
積分を行い、その値を格納する第１の手段と、 この第１の手段により求めた偏差の積分値を予じめ設定
した設定値にもとづいて評価することにより異常を検出
する第２の手段と、 前記第１の手段により求めた偏差の積分値の時間平均、
時系列データの微分値の積算により異常を定量化する第
３の手段と、 この第３の手段で演算されたデータから異常検出値、通
常積分値と異常検出されたいずれかの符号の積分値の比
較により水質の状態を評価する第４の手段と、 この第４の手段で評価された水質の状態を呈示する第５
の手段と、 を備えた水質異常検出装置。[Claims] Water quality data is inputted chronologically by a meter or by manual analysis,
A first means for integrating the deviation from the steady-state value in a range other than the noise width of the water quality data and independently integrating for the positive side and the negative side, and storing the values; a second means for detecting an abnormality by evaluating the integral value of the deviation obtained based on a preset setting value; and a time average of the integral value of the deviation obtained by the first means.
A third means of quantifying an abnormality by integrating differential values of time series data, and an abnormality detection value, a normal integral value and an integral value of any sign detected as an abnormality from the data calculated by this third means. a fourth means for evaluating the state of water quality by comparing; and a fifth means for presenting the state of water quality evaluated by this fourth means.
A water quality abnormality detection device comprising means for and.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP63261457A JP2578181B2 (en) | 1988-10-19 | 1988-10-19 | Water quality abnormality detection device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP63261457A JP2578181B2 (en) | 1988-10-19 | 1988-10-19 | Water quality abnormality detection device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH02108967A true JPH02108967A (en) | 1990-04-20 |
| JP2578181B2 JP2578181B2 (en) | 1997-02-05 |
Family
ID=17362162
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP63261457A Expired - Lifetime JP2578181B2 (en) | 1988-10-19 | 1988-10-19 | Water quality abnormality detection device |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP2578181B2 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000002784A (en) * | 1998-04-16 | 2000-01-07 | Toshiba Corp | Atmosphere monitor in PCV |
| JP2002122588A (en) * | 2000-10-17 | 2002-04-26 | Yokogawa Electric Corp | VOC environment monitoring system |
| CN118209595A (en) * | 2024-05-07 | 2024-06-18 | 江苏拓邦华创科技有限公司 | Pure water production terminal water quality detection system based on Internet of things |
| CN118420150A (en) * | 2024-04-25 | 2024-08-02 | 中国三冶集团有限公司 | A multi-directional monitoring system and method for medical wastewater treatment |
-
1988
- 1988-10-19 JP JP63261457A patent/JP2578181B2/en not_active Expired - Lifetime
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000002784A (en) * | 1998-04-16 | 2000-01-07 | Toshiba Corp | Atmosphere monitor in PCV |
| JP2002122588A (en) * | 2000-10-17 | 2002-04-26 | Yokogawa Electric Corp | VOC environment monitoring system |
| CN118420150A (en) * | 2024-04-25 | 2024-08-02 | 中国三冶集团有限公司 | A multi-directional monitoring system and method for medical wastewater treatment |
| CN118209595A (en) * | 2024-05-07 | 2024-06-18 | 江苏拓邦华创科技有限公司 | Pure water production terminal water quality detection system based on Internet of things |
Also Published As
| Publication number | Publication date |
|---|---|
| JP2578181B2 (en) | 1997-02-05 |
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