JP3716318B2 - Device remote diagnosis system - Google Patents

Description

臭化リチウムに関する物質収支は、
【数２】

熱収支は、
【数３】

【数４】

【数５】

【数６】

である。
ここで、Ｇは質量流量［ｋｇ／ｓ］、Ｈはエンタルピ［ｋＪ／ｋｇ］、Ｃは臭化リチウムの濃度［ｋｇ／ｋｇ］、ρは密度［ｋｇ／ｍ３］、Ｔは温度［℃］、Ｑｃは冷却水への伝熱量［ｋＪ／ｓ］、Ｖは吸収器の容積［ｍ^３］、Ａは伝熱面積［ｍ^２］、Ｕは総括伝熱係数［ｋＪ／（ｍ^２・ｓ・Ｋ）］、Ｐは圧力［Ｐａ］、Ｃｐは比熱［ｋＪ／ｋｇ・Ｋ］であり、添え字のＬは臭化リチウム水溶液、Ｗは冷却水、Ｓは蒸気、１及び２は位置（図９参照）をそれぞれ表している。圧力Ｐは温度Ｔ_Ｌ２と濃度Ｃ_Ｌ２で決まる臭化リチウム水溶液の蒸気圧力とする。 蒸発器５１０、再生器５４０、凝縮器５３０なども、同様に物理式でモデル化し、それぞれの収支式を連立させて計算することにより、シミュレーションが実施できる。
【００１２】
診断方法を説明する。機器が正常に運転している通常の場合は、動特性モデルの出力値と計測値が一致するようにモデルを調整しておく。完全に一致させることができなくても、正常時の誤差範囲を予め確認しておけばよい。機器に異常が発生すると、モデルの出力値と計測値に偏差が生じる、または、正常時の誤差範囲を逸脱して誤差が大きくなる。
シミュレーションを開始する時刻は、診断装置１４０が異常と判断する以前の正常状態の時刻からとする。従って図８に示すように、シミュレーション開始時にはモデル出力値と計測値は正常時の誤差範囲内で推移し、その後、異常発生に伴って誤差が拡大する。異常発生に伴って、どの状態量（例えば、圧力、温度、流量など）の計測値とシミュレーションの計算値との偏差が拡大していくかを検出する。例えば、冷却水管の伝熱性能が何らかの原因で劣化した場合を想定して、上記のモデル式でどのような現象が生じるかを考えてみる。
モデルでは、冷却水管の伝熱性能の劣化は（４）式におけるＵ（総括伝熱係数）が小さくなることと同じである。（４）式から伝熱量Ｑｃが小さくなり、冷却水出口温度Ｔ_Ｗ２が小さくなる筈である。また、Ｑｃが小さくなると、（３）式の右辺の値が大きくなるので、（３）式の左辺のｄ（ρＨ_Ｌ２）が大きくなる。Ｇ_Ｌ１，Ｇ_Ｌ２，Ｇｓに変化がないとすれば、（１）式よりρは変化しないので、エンタルピーＨ_Ｌ２すなわち臭化リチウム水溶液の出口温度Ｔ_Ｌ２が高くなる筈である。
従って、冷却水出口温度Ｔ_Ｗ２の低下と、臭化リチウム水溶液の出口温度Ｔ_Ｌ２の上昇が計測されると考えられる。一方、シミュレーションでは、正常な状態でシミュレーションしているので、Ｔ_Ｗ２の低下とＴ_Ｌ２の上昇はない筈であるから、これらの状態量を計測値と比較することによって、異常部位を推定することが可能である。
【００１３】
さて、モデルシミュレーションを実施する場合には、計算開始時点の状態を設定する必要がある。前述したように、遠隔施設内に初期値演算手段１５０を設け、ここで作成した初期値を監視センタ３で受信してシミュレーションを開始することが本発明の特徴である。
初期値演算手段１５０は、機器モデル３４０と同じモデルを持っており、診断対象機器１００への制御信号１１１または入力となる状態量の計測値１０１を取込んで実機の挙動を常にトレースしている。本例のモデルでは、燃焼器４０２への燃料流量調節弁４０６への開度指令値、タービン排ガス流量調節弁４１２の開度指令値、吸収式冷凍機の冷水流量調整弁５０２の開度指令及び冷却水流量の計測値を取込んでいる。これらの条件に合わせてシミュレーション演算をして実際の機器の動きをトレースするのである。そして、初期値演算手段１５０から５秒毎に初期値として必要なモデル内のプロセス変数値を出力し、初期値記憶手段１６０で所定期間分の初期値を常に保持する。
因みに、監視センタ３で初期値を演算する場合、常に遠隔地の機器の状態に機器モデル３４０を合わせておかなければならないため、常時、診断対象機器１００の制御信号等を受信しておく必要があり、そのため通信時間が長くなることにより通信費が高くなり、実用的でない。また、遠隔施設の数が増加すると、通信回線数も増加してさらに通信費がかかる他、監視センタ３では診断対象機器１００の台数分だけモデル計算を常に実施することになり、計算負荷の観点からもさらに実施困難である。例えば１００００施設以上におよぶ診断対象機器１００を有する場合には、監視センタ３で集中してモデル初期値を作成することは、これまで不可能であった。
本実施形態では、多数の診断対象機器１００を対象とした場合にも、遠隔施設毎に設けた初期値演算手段１５０によって実機の挙動を常にシミュレーション演算してトレースし、初期値を演算するため、遠隔施設内の機器についてその動特性モデルを用いた診断が可能になり、迅速かつ正確で詳細な診断が可能になると共に、その初期値は異常発生時のように必要な時に間欠的にその遠隔施設から監視センタ３に送信すれば良いので、通信費が安くなり、同時に監視センタ３の計算負荷を軽減することができる。
異常発生時のように必要な時に間欠的に任意の機器を対象にしたモデルシミュレーションを実施可能にすることにある。
また、初期値記憶手段１６０に所定期間分の初期値を保存しているので、記憶している時間内の任意の時間（の機器状態）からのシミュレーションを直ちに開始することができるため、診断に要する時間を大幅に短縮できる。
【００１４】
監視センタ３の表示画面を図６に示す。監視員は異常の通報を受けて、シミュレーションを実施する際に、計算開始時刻を設定する。図６では、時刻Ｃにおいてある状態量計測値（太線）が正常判定上限値を超えたために異常と判定されたものである。画面には、時刻Ｃ前後のプロセスの計測値がグラフ表示される。
初期値自動設定ボタン３６２をマウスでクリックすると、異常と判定した時刻Ｃから予め設定した時間前の時刻Ｂが選択される。しかし、時刻Ｂでは計測値が安定していないため、初期値としては不適当と監視員が判断した場合には、グラフ内の任意の場所をマウスでダブルクリックすることにより、そのマウスカーソルの位置に対応する時刻（例えば、時刻Ａ）に最も近い時刻の初期値データを選択し、時刻と初期値データ名を表示する。
計算開始時刻を選択した後、シミュレーション実行ボタン３６４をマウスでクリックすると、シミュレーション計算が実行される。シミュレーション結果は機器診断手段３５０へ送られる。機器診断手段３５０は全ての計測値と対応する計算値との偏差を時間毎に計算する。偏差が拡大した状態量の種類を偏差拡大の発生時間が早い順にならべて画面に表示する。また、監視員の操作によりそれぞれの状態量の時間変化をグラフ表示する。監視員は、これらの情報を参考にして異常部位を推定する。なお、機器診断手段３５０の機能として、偏差拡大の発生時間が早い順に並べた状態量の種類に応じて考えられる異常原因部位を推定するルールテーブルを予め設定しておき、異常原因部位の候補を自動的に表示するようにしても良い。
【００１５】
図２は、本発明の第２の実施形態を示す。第１の実施形態と異なる点は、診断装置１２０にモデル演算手段１６２とモデル調整手段１６４をさらに備えることである。
モデル特性を実機特性に合わせるために、モデル式中にモデル調整係数を導入する。例えば、（４）式中のＵ（総括熱伝達率）は正確な値を知ることが困難なので、次式のように調整係数Ｋｕを導入し、この係数値を調整することにより実機特性に合わせる。
【数７】

モデルの調整方法は、例えば特開平１０−２１４１１２号公報に記載されている方法を用いることで実現できる。すなわち、モデル調整手段１６４は定常状態とみなせる運転状態の運転データに基づいてその時の状態量計測値（例えば、臭化リチウム水溶液出口温度）に合うように、モデル調整係数Ｋｕの値を変化させる。モデル演算手段１６２はモデル調整手段１６４から新たな調整係数Ｋｕを受け取り、それを用いて再び温度を計算する。その計算結果をモデル調整手段１６４へ送り、前回の誤差と比較してモデル調整係数Ｋｕの値を修正する。この操作を繰り返して誤差がある範囲内に入ったところで調整終了とする。
ここでは、静特性の調整方法のみを記載したが、動特性についても、目標とする状態量の時間応答とモデルの計算結果との誤差が小さくなるように調整することが可能である。月日の経過に伴って、機器特性も変化する場合があるので、定期的にモデル調整を実施することが望ましい。
調整後のモデル調整係数Ｋｕは調整パラメータ記憶手段１６６に記憶される。異常発生時には、初期値と共に最新の調整係数を監視センタ３へ送信する。この場合は、監視センタ３の機器モデル３４０も（７）式のようにモデル調整係数を導入したモデル式になっており、受信した調整係数を用いてシミュレーションができる。
これにより、異常発生時に監視センタ３でシミュレーションする場合に、常に実機特性に合致したシミュレーションが可能になり、シミュレーションの精度が向上する。
【００１６】
図３及び図１０は、本発明の第３の実施形態を示す。第２の実施形態と異なる点は、診断装置１２０に遠隔指令受信手段１７２を設けたことと、監視センタ３の監視診断装置３１０に制御指令送信手段３２５を設けたことである。
異常発生の通報を受けて、監視センタ３では監視員が送られてくる運転データ等の情報や機器モデル３４０によるシミュレーション結果から機器の状態を判断する。
診断対象機器１００及びその制御装置１１０には安全装置（図示せず）が備えられ、発生した異常の程度によっては自ら緊急停止して危険を回避するようになっている。しかし、この安全装置が作動するのは、いわゆる重故障の場合であって全ての異常に対して動作するものではない。つまり、軽微な異常に対して全ての場合に緊急停止することは合理的でないため、重故障のみの対応になっている。一方、診断装置１２０による診断だけでは、診断対象機器１００の状態を正確に把握することは難しいため、診断装置１２０の診断結果は軽微故障であっても、診断対象機器１００を停止した方が良い場合もある。
本実施形態は、監視センタ３からの要請で派遣された保守員が現場に到着して診断対象機器１００の状況を調査してから、診断対象機器１００を停止させるか否かを判断するのでは対応が時間的に遅れるので、監視センタ３の監視員が判断して運転指令を出せるようにしたものである。
監視員が診断対象機器１００を停止させた方が良いと判断した場合は、監視センタ３のマンマシンインターフェイス３６０の緊急停止ボタンを押す。これにより制御指令送信手段３２５から停止指令信号１１２が送信され、通信ネットワーク２を介して対象施設Ａの診断装置１２０に伝達される。診断装置１２０は、遠隔指令受信手段１７２でその停止信号１１２を受信して制御装置１１０へ送る。制御装置１１０は、通常状態では自ら制御信号１１１を診断対象機器１００へ送っている。しかし、遠隔指令受信手段１７２で受信した停止信号１１２が送られてくると、緊急停止モードとり、制御信号１１１は停止信号になる。
【００１７】
以上は監視センタ３から停止指令を出して、診断対象機器１００を制御する場合であるが、監視センタ３からの指令は停止のみに限るものではない。例えば、第１の実施形態で述べたように、吸収器５２０内の冷却水伝熱管の伝熱性能が低化したと思われる場合には、冷却水流量を増加または減少させる指令値を送って診断対象機器１００の応答を見ることも可能である。
冷却水伝熱管の伝熱性能が低下している場合には、冷却水流量の変化に対する臭化リチウム水溶液の出口温度Ｔ_Ｌ２の変化率が正常時よりも小さくなる筈である。監視センタ３からの冷却水流量変更指令によりこの現象が確認できれば、冷却水管の伝熱性能劣化という異常原因推定結果がより確からしくなる。
そのためには、正常時の冷却水流量と臭化リチウム水溶液出口温度との応答特性を調査しておくことが望ましいが、それがなくても、機器モデル３４０で冷却水流量を同じように変化させた状態でシミュレーションし、その結果と比較すれば良い。図１１は、シミュレーションによる計算値と実際の機器の計測値を比較してマンマシンインターフェイス３６０の表示画面に表示した画面の図である。
以上のようにして、例えば冷却水管の伝熱性能劣化という異常原因が特定できたら、（４）式中のＵの値または、（７）式中のＫｕの値を一次的に小さい値に変更してシミュレーションすることにより、異常事象を模擬したシミュレーションが可能である。従って、シミュレーションにより、今後、時間と共に機器の状態がどのように変化していくかを予測することができる。監視員は、この予測結果に基づいて機器を早急に停止させた方が良いのか、あるいは運転継続可能かを判断することもできる。
また、条件（この場合は吸収器の冷却水流量）を変更してシミュレーションを実施する際に、診断対象機器１００の主要な出力値への影響を見ることが望ましい。冷凍機の場合は主要な出力は冷水である。冷却水流量を変更すれば、需要家へ供給する冷水の温度も変化する。冷水は施設内の冷房や食品の冷蔵などに使われている場合が多い。冷却水流量の変更により、冷水温度が極端に上昇することは避けなければならない。シミュレーションにより供給する冷水温度の変化を計算することによって、需要家にとって許容できる冷却水流量の変化幅や変更する時間間隔を決めることができる。
【００１８】
【発明の効果】
以上説明したように、本発明によれば、遠隔地に散在する多数の機器を監視センタで各機器の動特性モデルを用いて診断する際に、遠隔施設毎に設けた初期値演算手段によって実機の挙動を所定の時間間隔でシミュレーション演算してトレースし、初期値を演算するため、遠隔施設内の機器についてその動特性モデルを用いた診断が可能になり、迅速かつ正確で詳細な診断が可能になると共に、その初期値は異常発生時のように必要な時に間欠的にその遠隔施設から監視センタに送信すれば良いので、通信時間が短縮され、これによって通信費が安くなり、また、遠隔施設毎に施設内で初期値を演算するため、監視センタの計算負荷を軽減することができる。
また、遠隔施設毎に設けた初期値演算手段によって遠隔地に散在するいずれの機器に対しても迅速かつ正確なシミュレーションが可能になるため、シミュレーション結果を用いた機器診断の精度を向上させることができる。
また、初期値記憶手段に所定期間分の初期値を保存するため、記憶している時間内の任意の時間（の機器状態）からのシミュレーションを直ちに開始することができ、診断に要する時間を大幅に短縮することができる。
また、機器モデル特性を実機特性に合わせるために、モデル式中にモデル調整係数を導入することにより、異常発生時に監視センタで常に実機特性に合致したシミュレーションが可能になり、シミュレーションの精度を向上させることができる。
また、遠隔施設に遠隔指令受信手段、監視センタに制御指令送信手段を設けることにより、異常事象を模擬したシミュレーションに基づいて緊急に対応処置を講じることができ、また、この両手段を介して遠隔施設の機器に制御指令値を送ってその応答を見ることにより、異常原因を迅速に推定することが可能になる。
【図面の簡単な説明】
【図１】 本発明の第１の実施形態を示す機器の遠隔診断システム
【図２】 本発明の第２の実施形態
【図３】 本発明の第３の実施形態
【図４】 本発明の対象施設の機器構成図
【図５】 吸収式冷凍機の構造図
【図６】 本発明のシミュレーション開始時刻選択画面を表す図
【図７】 本発明の主な計測項目を説明する図
【図８】 本発明の計測値とモデル計算値の挙動を説明する図
【図９】 吸収式冷凍機の吸収器モデルの説明図
【図１０】 本発明の第３の実施形態
【図１１】 本発明の診断画面の表示例図
【符号の説明】
１ａ，１ｂ，１ｃ，１ｎ…遠隔施設、２…通信ネットワーク、３…監視装置、１００…診断対象機器、１１０…制御装置、１２０…診断装置、１３０…運転データ記憶手段、１４０…状態判定手段、１５０…初期値演算手段、１６０…初期値記憶手段、１６２…モデル演算手段、１６４…モデル調整手段、１６６…調整パラメータ記憶手段、１７０…データ送信手段、１７２…遠隔指令受信手段１７２、３１０…監視診断装置、３２０…データ受信手段、３２５…制御指令送信手段、３３０…データ記憶手段、３４０…機器モデル、３５０…機器診断手段、３６０…マンマシンインターフェイス[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a remote monitoring system that monitors or controls a device installed in a remote place, and more particularly, to a remote diagnosis system for a device that diagnoses a device using a dynamic characteristic model of the device.
[0002]
[Prior art]
The following (1) is a conventional technique for remotely monitoring a device via a communication means. Further, there are the following (2) and (3) regarding the initial value generation method related to the simulation using the device model. Further, the remote diagnosis technology using model simulation includes the following (4).
(1) Technology described in JP-A-9-26237, (2) Technology described in JP-A-6-222191, (3) Technology described in JP-A-11-305646, (4) Technology described in Japanese Patent Publication No. 9-305429
In the prior art (1), a terminal device is attached to each refrigerator and a physical quantity obtained by detecting the state of each of the devices constituting the refrigerator and a signal indicating the operation state of the entire refrigerator are calculated and controlled by the terminal device. The above physical quantities and signals are periodically stored in a storage device, and the correlation between these physical quantities and signals is determined and output to a transmission means, which is provided in a plurality of buildings. The central monitoring device for managing the refrigerators receives the determined information and the physical quantities and signals via the communication means of each terminal device using the remote communication means, and the received information is the central monitoring device It is analyzed, displayed and recorded by a computer provided in.
In the prior art (2), when an abnormal state occurs in an actual plant and the abnormal state is determined by the diagnostic device, when the operator requests a prediction re-operation to the prediction simulator, The internal data of the prediction simulator is reset as the initial value of the prediction simulator, and the prediction simulator is restarted.
Prior art (3) applies the node junction method, which simulates the plant piping base point as a node and the flow path as a junction for the fluid flowing in the plant piping, and solves the differential equation for heat flow simulation. And input / output model nodding data, actual process measurement point data, design data, and reliability data representing the accuracy of the data added to the design data, and accurately based on the input / output data. In this example, the initial data of the model is generated by correcting the non-standard data and generating the optimal initial value data.
In the prior art (4), data related to a failure that occurred in a remotely diagnosed device is accumulated on the diagnosed device side, and acquired after the diagnostic device of the maintenance center has acquired the data via a communication line. The device to be diagnosed is diagnosed by simulation based on the data.
[0003]
[Problems to be solved by the invention]
In order to perform a simulation using a dynamic characteristic model, an initial value for determining an initial state of the model is necessary. When a dynamic characteristic model is composed of a mathematical model including a differential equation, an initial value is always required to solve the differential equation.
When a device is diagnosed using the dynamic characteristic model, the accuracy of the diagnosis is greatly affected by an error between the dynamic characteristic of the actual machine and a calculated value of the characteristic using the model, that is, the accuracy of the model. In order to match the calculated value of the dynamic characteristics with the actual machine characteristics, it is not sufficient to input the input value given to the device into the model. If the initial state (initial state) to start the calculation is not matched to the actual machine Don't be. That is, it is an important point whether the initial value of a model can be set appropriately. If a dynamic characteristic model with high accuracy is used, the accuracy of determination as to whether or not the device is normal can be improved, and further detailed diagnosis such as identification of the cause of the abnormality and the ripple effect of the abnormality can be performed.
Now, in a system that uses a dynamic characteristic model at a monitoring center for devices in remote locations, the operation data is sent to the monitoring center via a communication line, and the initial value of the device model is created on the monitoring center side. Is possible. In order to always calculate the initial value for every target device, there is a method to always receive the operation data of all target devices and calculate the initial value, but this method causes excessive communication load and communication cost. There is a problem. Moreover, if all the device models are always implemented in real time in the monitoring center, it is not necessary to calculate the initial value, but it is not realistic because the load on the computer increases. In particular, when the number of target devices increases, the tendency becomes remarkable. Actually, remote monitoring / diagnosis of devices is a useful method because it is a centralized monitoring / diagnosis of a large number of devices installed in remote locations. The challenge is a serious problem.
Therefore, even if there is a technology for diagnosing equipment using a dynamic characteristic model as in the above-mentioned prior art and a technique for remotely monitoring equipment information by communication, the dynamic characteristic model of the target equipment is It is practically difficult to diagnose the device.
[0004]
In view of the above circumstances, the problem of the present invention is that in a remote diagnosis system for a device that targets a large number of devices, it is possible to perform a model simulation that targets any device intermittently when necessary, such as when an abnormality occurs. There is to do.
[0005]
[Means for Solving the Problems]
In order to solve the above problem, the monitoring diagnostic device of the monitoring center has a device model simulating the dynamic characteristics of the device to be diagnosed, and the remote in-house diagnostic device in which the device to be diagnosed is installed is a measurement of the device. An initial value calculating means for calculating an independent variable value (initial value) for setting the initial state of the device model in the device state based on the information at a predetermined time interval; and the initial value calculating means At predetermined time intervals An initial value storage means for storing and storing the calculated initial value for a predetermined time; Provided with a state determination means for determining whether or not the state of the device is normal based on the process value measurement data included in the device information related to the operating state, The monitoring / diagnosis device receives the model initial value from the in-facility diagnostic device, and uses the model initial value to calculate the dynamic characteristics of the device based on the device model. When the device state is determined to be abnormal by the state determination unit, the initial value storage unit transmits a model initial value at a predetermined time before the occurrence of the abnormality to the monitoring diagnostic device, and the device model Perform equipment simulation .
[0006]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings. As an embodiment of the present invention, a remote diagnosis system of the present invention applied to a remote monitoring system that centrally monitors cogeneration facilities installed at a plurality of remote locations will be described.
First, the cogeneration facility that is the object of remote monitoring will be described. Cogeneration facilities have been introduced into various types of facilities such as general buildings, supermarkets, store buildings such as convenience stores, and accommodation facilities such as hotels, and each facility is located geographically apart. An example will be described below, although the configuration and capacity of the device differ depending on the facility.
As shown in FIG. 4, the cogeneration facility (diagnosis target device 100) includes a gas turbine generator including a compressor 400, a combustor 402, a turbine 404 and a generator 408, and a high-temperature gas 410 discharged from the turbine 404. The system includes a vapor absorption refrigerator 500 that generates cold as a heat source, and a control device 110 for controlling these devices and a device status diagnosis device 120 are connected to each other.
The gas turbine generator sucks and compresses air with the compressor 400 and sends the compressed air to the combustor 402. The combustor 402 combusts the fuel supplied by operating the fuel control valve 406. The combustion gas rotates the turbine 404 in the process of expanding, and rotates the generator 408 with the rotational force to obtain an electrical output. The combustion gas 410 that has worked in the turbine 404 is discharged at a high temperature of several hundred degrees C. Therefore, the heat is recovered and the absorption chiller 500 is further driven to produce cold water 504. The electricity generated by the generator 408 is supplied to the electricity demand in the facility, the excess is sold to the power company, and the shortage is contracted to purchase electricity from the power company as usual. The cold produced by the absorption chiller 500 is consumed by the cold demand of the air conditioner or refrigerator in the facility.
Therefore, cogeneration equipment can supply both electricity and heat energy required in the facility, and the surplus power can be sold, making it an advantageous system for the facility owner. .
[0007]
The operation principle of the absorption chiller 500 will be described with reference to FIG. The absorption refrigerator 500 mainly includes an evaporator 510, an absorber 520, a condenser 530, a regenerator 540, heat exchangers 550 and 555, and fluid pumps 560 and 565. The refrigerator of this example uses a lithium bromide solution as an absorbing solution and water as a refrigerant.
In the regenerator 540, the lithium bromide aqueous solution having a reduced concentration by absorbing water as a refrigerant is heated to evaporate the water in the solution and concentrate the solution. The high temperature exhaust gas 410 from the turbine 404 is used as this heating source. Heat exchange is performed between the turbine exhaust gas 410 and the high-concentration lithium bromide aqueous solution to heat the lithium bromide aqueous solution. The water evaporated in the regenerator 540 flows to the condenser 530, is concentrated by heating, and the lithium bromide aqueous solution whose temperature has been increased decreases the temperature through the high temperature heat exchanger 550 and the low temperature heat exchanger 555, and the absorber 520 It is sprayed in.
The condenser 530 condenses the steam generated in the regenerator 540 by heat exchange with cooling water and returns it to water (liquid). The condensed water is scattered in the evaporator 510. In the evaporator 510, a cold water pipe is arranged, and the sprayed water takes heat from the cold water pipe and evaporates to become steam again. As a result, the temperature of the chilled water in the chilled water pipe is lowered and supplied to the chilled water demand 600 such as air conditioner as about 7 ° C. water. The water that has not evaporated once accumulates in the lower part of the evaporator 510 and is again sprayed into the container from the upper part of the evaporator 510 by the refrigerant circulation pump 565.
The evaporated vapor is absorbed in contact with the high concentration lithium bromide aqueous solution sprayed into the absorber 520. As the vapor is absorbed, the pressure in the absorber 520 decreases. Accordingly, the pressure in the evaporator 510 connected to the absorber 520 is also reduced, so that water as a refrigerant evaporates at a low temperature in the evaporator 510. Since the lithium bromide aqueous solution absorbs vapor more easily as the temperature is lower, the lithium bromide aqueous solution is cooled with cooling water in the absorber 520. Thereafter, as described above, the cooling water condenses the vapor in the condenser 530 and the temperature further rises, so that the cooling water is cooled in the cooling tower 700 and returned to the absorber 520 again. The aqueous lithium bromide solution whose concentration has been reduced by the absorber 520 is heated by the low temperature heat exchanger 555 and the high temperature heat exchanger 550 and returns to the regenerator 540. The absorption refrigerator 500 repeats the above cycle to generate cold.
Normally, the exhaust gas 410 from the turbine 404 is used as a heat source for the absorption chiller 500. However, when the turbine is stopped for some reason, the exhaust gas 410 serving as a heat source cannot be received, and the absorption chiller 500 cannot be operated. Therefore, the regenerator 570 of the absorption chiller 500 is provided with a burner 570 serving as an alternative heat source.
[0008]
This cogeneration facility is controlled by the control device 110. In order to control the output of the generator 408, the fuel flow rate supplied to the combustor 402 is adjusted by manipulating the opening of the fuel control valve 406. Further, an exhaust gas flow rate serving as a heat source is controlled by a gas flow rate control valve 412 in order to control the load of the absorption chiller 500. Although other detailed control methods are omitted, the control device 110 captures information necessary for control, for example, measurement values such as a generator output and a cold water temperature. 4 and 5, only measurement signals from some measuring instruments are shown.
In order to determine whether the state of the device is normal or abnormal, the diagnostic device 120 uses sensor information taken in the control device 110 and a control command value from the control device 110, as well as other sensors attached to the device. Signals for diagnosis are captured.
[0009]
FIG. 1 shows the configuration of a remote diagnosis system for equipment according to a first embodiment of the present invention. The monitoring center 3 can communicate with remote facilities 1a, 1b, 1c, and 1n via the communication network 2. Here, although the communication network 2 uses a public telephone line, other communication means using a dedicated communication cable or satellite may be used, and the present invention does not depend on the type of communication means. In FIG. 1, four remote facilities 1a, 1b, 1c, and 1n are shown, but there are actually 10,000 or more facilities. The present invention is not limited by the number of remote facilities.
The remote facility 1a includes a diagnosis target device 100, a control device 110 that controls the device, and a diagnosis device 120 that diagnoses the device state. Although omitted in FIG. 1, devices having the same configuration exist in the facilities 1b, 1c, and 1n.
As described above, the diagnosis target device 100 in the facility 1a is a cogeneration facility including a turbine generator and an absorption refrigerator. The control device 110 takes in measurement data necessary for device control from various sensors attached to the diagnosis target device 100, calculates a control signal 111, and outputs it to the diagnosis target device 100. In the diagnosis target device 100, each actuator is operated by the control signal 111. The diagnosis device 120 takes in measurement data (measurement value) 101 for diagnosis, measurement data (measurement value) 102 necessary for diagnosis other than that, and a control signal 111 from the control device 110. Those data are sequentially stored in the operation data storage means 130. Data is measured at a cycle of one second, and data for about one week can be stored in the data storage means 130. When the storage capacity of the data storage means 130 is full, the oldest data is deleted sequentially and new data is added. The operation data from the operation data storage unit 130 is sequentially sent to the state determination unit 140 to diagnose the device state online. The main measurement items used for determination by the state determination means 140 are shown in FIG. The diagnosis method determines whether or not the measured value exceeds the upper and lower limit values determined in advance for each measurement item. If any one of the values exceeds the upper and lower limit values, it is regarded as abnormal and the alarm lamp of the device is turned on, and the measured value is reported to the monitoring center. Along with the notification, the operation data before and after the occurrence of the abnormality is transmitted to the monitoring center 3.
[0010]
The monitoring center 3 receives the information by the data receiving means 320 via the communication network 2. The received information is stored in the database 330. Data before and after the occurrence of abnormality is recorded in the database 330 for each facility. The result of diagnosis by the state determination unit 140 is output on the display screen of the man-machine interface 360. In addition, the measurement values before and after the occurrence of an abnormality are displayed in a graph by the operation of the monitor. The observer estimates the cause of the abnormality from the data and considers repair measures. In addition, a maintenance staff is dispatched to the facility 1a by contacting a maintenance center adjacent to the facility where the abnormality has occurred (in this example, the facility 1a). In order for maintenance personnel to be able to quickly respond to equipment abnormalities, it is desirable that appropriate information regarding the state of the equipment is obtained. If you know the degree and cause of the abnormality before you go to the site, there is a merit that you can arrange replacement parts early. By doing so, the repair of the equipment can be completed in a short time, and early recovery is possible.
Accordingly, it is important to promptly transmit appropriate instructions and operation data analysis results from the monitoring center 3 to maintenance personnel. If the estimation result of the monitoring center 3 is correct, the maintenance staff only needs to act according to the instruction of the monitoring center, and can provide a stable maintenance service regardless of the experience and technical capabilities of the person in charge.
However, since the monitoring center 3 cannot directly check an abnormal device, it is necessary to judge the situation and estimate the cause of the abnormality from the limited information sent by communication. In addition, since the monitoring staff monitors a large number of facilities, many types, and a large number of devices, it is impossible to always grasp the characteristics and operating states of all the devices. Therefore, it is very difficult to accurately recognize the state of the device and estimate the cause of the abnormality when the abnormality occurs. Therefore, as described in Japanese Patent Application Laid-Open No. 9-305429 and Japanese Patent Application Laid-Open No. 8-219601, there is a method of performing a diagnosis by performing a simulation at a monitoring center.
Also in this embodiment, the monitoring center 3 is provided with the device model 340, the device is simulated, and the behavior of the simulation result is compared with the actual measurement data to estimate the abnormal part.
However, in this embodiment, the initial value calculation means 150 is provided in the diagnostic apparatus 120 in the remote facility A, and the initial value calculation means 150 generates an initial value of a model necessary for the simulation by the device model 340. The initial value calculation means 150 outputs the initial value of the time at intervals of 5 seconds. Similarly, the initial value storage unit 160 is provided in the diagnosis apparatus 120 in the remote facility A, and the initial value output from the initial value calculation unit 150 is held in the initial value storage unit 160 for a predetermined time interval. Similar to the operation data storage unit 130, the initial value storage unit 160 deletes old initial value data and stores new initial value data when a predetermined time has elapsed. The initial value data stored in the initial value storage means 160 is transmitted through the data transmission means 170 as necessary and sent to the monitoring center 3.
Thus, the present embodiment is characterized in that the initial value calculation means 150 and the initial value storage means 160 are provided in the diagnostic device 120 in the remote facility A. In the monitoring center 3, the device model 340 and the remote facility A The simulation is started from the device state at an arbitrary time within the time stored in the initial value storage unit 160 using the initial value data generated by the initial value calculation unit 150.
[0011]
In this embodiment, a dynamic characteristic model in which the characteristics of the device are described by physical formulas is used. Hereinafter, a diagnostic method using this model will be described.
The dynamic model of equipment is simulated by physical formula. For example, the dynamic characteristics of the absorber 520 are modeled as shown in FIG. The model formula is shown below.
The material balance is
[Expression 1]

The material balance for lithium bromide is
[Expression 2]

The heat balance is
[Equation 3]

[Expression 4]

[Equation 5]

[Formula 6]

It is.
Here, G is mass flow rate [kg / s], H is enthalpy [kJ / kg], C is lithium bromide concentration [kg / kg], ρ is density [kg / m3], and T is temperature [° C.]. , Qc is the heat transfer amount to the cooling water [kJ / s], V is the volume of the absorber [m ³ ], A is the heat transfer area [m ² ], U is the overall heat transfer coefficient [kJ / (m ² S · K)], P is pressure [Pa], Cp is specific heat [kJ / kg · K], subscript L is lithium bromide aqueous solution, W is cooling water, S is steam, 1 and 2 are Each position (see FIG. 9) is shown. Pressure P is temperature T _L2 And concentration C _L2 The vapor pressure of the lithium bromide aqueous solution determined by The evaporator 510, the regenerator 540, the condenser 530, and the like can be similarly modeled by physical formulas, and simulations can be performed by calculating each balance equation simultaneously.
[0012]
A diagnosis method will be described. When the device is operating normally, the model is adjusted so that the output value of the dynamic characteristic model matches the measured value. Even if they cannot be completely matched, it is sufficient to confirm the error range in the normal state in advance. When an abnormality occurs in the device, a deviation occurs between the output value and the measured value of the model, or the error increases outside the normal error range.
The time for starting the simulation is assumed to be from the time of the normal state before the diagnosis device 140 determines that the abnormality is present. Therefore, as shown in FIG. 8, the model output value and the measured value change within the normal error range at the start of the simulation, and thereafter, the error increases as the abnormality occurs. As the abnormality occurs, it is detected which state quantity (for example, pressure, temperature, flow rate, etc.) and the deviation between the measured value and the calculated value of the simulation increase. For example, assuming that the heat transfer performance of the cooling water pipe has deteriorated for some reason, let us consider what phenomenon occurs in the above model formula.
In the model, the deterioration of the heat transfer performance of the cooling water pipe is the same as the decrease of U (overall heat transfer coefficient) in the equation (4). From equation (4), the heat transfer amount Qc decreases, and the cooling water outlet temperature T _W2 Should be smaller. Also, as Qc decreases, the value on the right side of equation (3) increases, so d (ρH on the left side of equation (3) _L2 ) Becomes larger. G _L1 , G _L2 , Gs does not change, ρ does not change from equation (1). _L2 That is, the outlet temperature T of the lithium bromide aqueous solution _L2 Should be higher.
Therefore, the cooling water outlet temperature T _W2 And the outlet temperature T of the aqueous lithium bromide solution _L2 It is thought that the rise of On the other hand, since the simulation is performed in a normal state, T _W2 Decline and T _L2 Therefore, it is possible to estimate an abnormal part by comparing these state quantities with measured values.
[0013]
Now, when performing model simulation, it is necessary to set the state at the time of starting calculation. As described above, it is a feature of the present invention that the initial value calculation means 150 is provided in the remote facility, the initial value created here is received by the monitoring center 3 and the simulation is started.
The initial value calculation means 150 has the same model as the device model 340, and takes in the control signal 111 to the diagnosis target device 100 or the measurement value 101 of the state quantity to be input and always traces the behavior of the actual machine. . In the model of this example, the opening command value to the fuel flow rate adjustment valve 406 to the combustor 402, the opening command value to the turbine exhaust gas flow rate adjustment valve 412, the opening command to the cold water flow rate adjustment valve 502 of the absorption chiller, and The measured value of the cooling water flow rate is taken in. A simulation operation is performed according to these conditions to trace the actual movement of the device. Then, a necessary process variable value in the model is output as an initial value every 5 seconds from the initial value calculation means 150, and the initial value storage means 160 always holds the initial value for a predetermined period.
Incidentally, when the initial value is calculated in the monitoring center 3, it is necessary to always match the device model 340 with the state of the remote device, and therefore it is necessary to always receive the control signal of the device 100 to be diagnosed. Therefore, the communication cost is increased due to the longer communication time, which is not practical. Further, when the number of remote facilities increases, the number of communication lines also increases and communication costs are increased. In addition, the monitoring center 3 always performs model calculation for the number of devices 100 to be diagnosed. It is even more difficult to implement. For example, in the case of having the diagnosis target devices 100 having 10000 facilities or more, it has been impossible until now to create model initial values in the monitoring center 3 in a concentrated manner.
In the present embodiment, even when a large number of diagnosis target devices 100 are targeted, the initial value calculation means 150 provided for each remote facility always simulates and traces the behavior of the actual machine, and calculates the initial value. It is possible to diagnose the equipment in the remote facility using its dynamic characteristic model, and it is possible to make a quick, accurate and detailed diagnosis. Since it suffices to transmit from the facility to the monitoring center 3, the communication cost is reduced, and at the same time, the calculation load on the monitoring center 3 can be reduced.
It is to enable a model simulation for an arbitrary device intermittently when necessary, such as when an abnormality occurs.
In addition, since the initial value for a predetermined period is stored in the initial value storage unit 160, a simulation from any time (device state) within the stored time can be started immediately. The time required can be greatly reduced.
[0014]
A display screen of the monitoring center 3 is shown in FIG. The monitor receives a report of abnormality and sets the calculation start time when performing the simulation. In FIG. 6, the state quantity measurement value (thick line) at time C is determined to be abnormal because it exceeds the normal determination upper limit value. The measured values of the processes before and after time C are displayed in a graph on the screen.
When initial value automatic setting button 362 is clicked on with a mouse, time B before the preset time from time C determined to be abnormal is selected. However, since the measured value is not stable at time B, if the monitor determines that the initial value is inappropriate, the position of the mouse cursor can be determined by double-clicking with the mouse anywhere in the graph. The initial value data at the time closest to the time corresponding to (for example, time A) is selected, and the time and the initial value data name are displayed.
After selecting the calculation start time, click on the simulation execution button 364 with the mouse to execute the simulation calculation. The simulation result is sent to the device diagnosis unit 350. The device diagnosis unit 350 calculates the deviation between all the measured values and the corresponding calculated values every time. The types of state quantities with increased deviations are displayed on the screen in order of the occurrence time of deviation enlargement. Moreover, the time change of each state quantity is displayed as a graph by the operation of the monitor. The monitor estimates the abnormal site with reference to this information. In addition, as a function of the device diagnosis unit 350, a rule table for estimating an abnormal cause site that is considered according to the type of state quantity arranged in the order of occurrence of deviation expansion is set in advance, and candidates for the abnormal cause site are set. You may make it display automatically.
[0015]
FIG. 2 shows a second embodiment of the present invention. The difference from the first embodiment is that the diagnostic apparatus 120 further includes a model calculation unit 162 and a model adjustment unit 164.
In order to match the model characteristics to the actual machine characteristics, a model adjustment coefficient is introduced into the model formula. For example, since it is difficult to know an accurate value of U (overall heat transfer coefficient) in equation (4), an adjustment coefficient Ku is introduced as in the following equation, and this coefficient value is adjusted to match the actual machine characteristics. .
[Expression 7]

The model adjustment method can be realized by using, for example, a method described in JP-A-10-214112. That is, the model adjustment unit 164 changes the value of the model adjustment coefficient Ku based on the operation data in the operation state that can be regarded as the steady state so as to match the state quantity measurement value (for example, the lithium bromide aqueous solution outlet temperature) at that time. The model calculation means 162 receives a new adjustment coefficient Ku from the model adjustment means 164, and uses it to calculate the temperature again. The calculation result is sent to the model adjustment means 164, and the value of the model adjustment coefficient Ku is corrected by comparing with the previous error. This operation is repeated and the adjustment ends when the error falls within a certain range.
Although only the method for adjusting the static characteristic is described here, the dynamic characteristic can also be adjusted so that the error between the time response of the target state quantity and the calculation result of the model is small. Since the device characteristics may change with the passage of the month and day, it is desirable to perform model adjustment periodically.
The adjusted model adjustment coefficient Ku is stored in the adjustment parameter storage unit 166. When an abnormality occurs, the latest adjustment coefficient is transmitted to the monitoring center 3 together with the initial value. In this case, the device model 340 of the monitoring center 3 is also a model formula in which a model adjustment coefficient is introduced as in Expression (7), and simulation can be performed using the received adjustment coefficient.
As a result, when a simulation is performed at the monitoring center 3 when an abnormality occurs, a simulation always matching the actual machine characteristics is possible, and the accuracy of the simulation is improved.
[0016]
3 and 10 show a third embodiment of the present invention. The difference from the second embodiment is that a remote command receiving unit 172 is provided in the diagnostic device 120 and a control command transmission unit 325 is provided in the monitoring diagnostic device 310 of the monitoring center 3.
In response to the notification of the occurrence of abnormality, the monitoring center 3 determines the state of the device from information such as operation data sent by the monitoring staff and the simulation result by the device model 340.
The diagnosis target device 100 and its control device 110 are provided with a safety device (not shown), and depending on the degree of abnormality that has occurred, the device itself is urgently stopped to avoid danger. However, this safety device operates in the case of a so-called serious failure and does not operate for all abnormalities. That is, it is not reasonable to make an emergency stop in all cases for minor abnormalities, so only a major failure is dealt with. On the other hand, since it is difficult to accurately grasp the state of the diagnosis target device 100 only by the diagnosis by the diagnosis device 120, it is better to stop the diagnosis target device 100 even if the diagnosis result of the diagnosis device 120 is a minor failure. In some cases.
In the present embodiment, the maintenance staff dispatched at the request from the monitoring center 3 arrives at the site and investigates the status of the diagnosis target device 100, and then determines whether or not to stop the diagnosis target device 100. Since the response is delayed in time, the supervisor of the monitoring center 3 can judge and issue an operation command.
When it is determined that the monitor should stop the diagnosis target device 100, the emergency stop button of the man-machine interface 360 of the monitoring center 3 is pressed. As a result, the stop command signal 112 is transmitted from the control command transmission means 325 and transmitted to the diagnostic apparatus 120 of the target facility A via the communication network 2. The diagnostic device 120 receives the stop signal 112 by the remote command receiving means 172 and sends it to the control device 110. The control device 110 sends the control signal 111 to the diagnosis target device 100 in the normal state. However, when the stop signal 112 received by the remote command receiving means 172 is sent, the emergency stop mode is taken and the control signal 111 becomes a stop signal.
[0017]
The above is a case in which a stop command is issued from the monitoring center 3 to control the diagnosis target device 100, but the command from the monitoring center 3 is not limited to the stop. For example, as described in the first embodiment, when it is considered that the heat transfer performance of the cooling water heat transfer pipe in the absorber 520 is lowered, a command value for increasing or decreasing the cooling water flow rate is sent. It is also possible to see the response of the diagnosis target device 100.
When the heat transfer performance of the cooling water heat transfer tube is deteriorated, the outlet temperature T of the aqueous solution of lithium bromide with respect to the change of the cooling water flow rate _L2 The rate of change should be smaller than normal. If this phenomenon can be confirmed by the cooling water flow rate change command from the monitoring center 3, the abnormal cause estimation result of the deterioration of the heat transfer performance of the cooling water pipe becomes more accurate.
To that end, it is desirable to investigate the response characteristics between the normal cooling water flow rate and the lithium bromide aqueous solution outlet temperature, but even without this, the equipment model 340 can be used to change the cooling water flow rate in the same way. It is only necessary to perform simulation in the state and compare with the result. FIG. 11 is a diagram of a screen displayed on the display screen of the man-machine interface 360 by comparing the calculated value obtained by the simulation with the measured value of the actual device.
As described above, for example, when the cause of abnormality such as deterioration of the heat transfer performance of the cooling water pipe can be identified, the value of U in the equation (4) or the value of Ku in the equation (7) is changed to a primary small value. By simulating, it is possible to simulate an abnormal event. Therefore, it is possible to predict how the state of the device will change with time by simulation. The monitor can determine whether it is better to stop the device immediately based on the prediction result or whether the operation can be continued.
In addition, it is desirable to see the influence on the main output value of the diagnosis target device 100 when the simulation is performed by changing the condition (in this case, the cooling water flow rate of the absorber). In the case of a refrigerator, the main output is cold water. If the cooling water flow rate is changed, the temperature of the cold water supplied to the consumer also changes. Cold water is often used for cooling the facilities and refrigeration of food. It must be avoided that the temperature of the chilled water rises excessively due to the change of the cooling water flow rate. By calculating the change in the temperature of the chilled water supplied by the simulation, it is possible to determine the change width of the cooling water flow rate acceptable for the consumer and the time interval to be changed.
[0018]
【The invention's effect】
As described above, according to the present invention, when a large number of devices scattered in a remote place are diagnosed by using a dynamic characteristic model of each device at a monitoring center, the actual value is calculated by the initial value calculation means provided for each remote facility. The behavior of At predetermined time intervals Since simulation calculation and tracing are performed, and initial values are calculated, it is possible to diagnose the equipment in the remote facility using its dynamic characteristic model, enabling quick, accurate and detailed diagnosis, and the initial value is Since it is sufficient to intermittently transmit from the remote facility to the monitoring center when necessary, such as when an abnormality occurs, the communication time is shortened, thereby reducing the communication cost, and for each remote facility In the facility Since the initial value is calculated, the calculation load on the monitoring center can be reduced.
In addition, since the initial value calculation means provided for each remote facility enables a quick and accurate simulation for any device scattered in a remote place, the accuracy of device diagnosis using the simulation result can be improved. it can.
In addition, since the initial value for a predetermined period is stored in the initial value storage means, a simulation from any time within the stored time (device state) can be started immediately, greatly increasing the time required for diagnosis Can be shortened.
In addition, by introducing a model adjustment coefficient in the model formula to match the device model characteristics to the actual machine characteristics, it is possible to perform a simulation that always matches the actual machine characteristics at the monitoring center when an abnormality occurs, improving the accuracy of the simulation. be able to.
Further, by providing a remote command receiving means at the remote facility and a control command transmitting means at the monitoring center, it is possible to take an urgent response action based on a simulation simulating an abnormal event. By sending a control command value to the facility equipment and looking at the response, it is possible to quickly estimate the cause of the abnormality.
[Brief description of the drawings]
FIG. 1 is a remote diagnosis system for an apparatus according to a first embodiment of the present invention.
FIG. 2 is a second embodiment of the present invention.
FIG. 3 is a third embodiment of the present invention.
[Fig. 4] Device configuration diagram of the target facility of the present invention
[Fig.5] Structure of absorption refrigerator
FIG. 6 is a diagram showing a simulation start time selection screen according to the present invention.
FIG. 7 is a diagram for explaining main measurement items of the present invention.
FIG. 8 is a diagram for explaining the behavior of measured values and model calculated values according to the present invention.
FIG. 9 is an explanatory diagram of an absorber model of an absorption refrigerator
FIG. 10 is a third embodiment of the present invention.
FIG. 11 is a display example diagram of the diagnosis screen of the present invention.
[Explanation of symbols]
1a, 1b, 1c, 1n ... remote facility, 2 ... communication network, 3 ... monitoring device, 100 ... diagnostic device, 110 ... control device, 120 ... diagnostic device, 130 ... operation data storage means, 140 ... state determination means, 150 ... initial value calculation means, 160 ... initial value storage means, 162 ... model calculation means, 164 ... model adjustment means, 166 ... adjustment parameter storage means, 170 ... data transmission means, 172 ... remote command reception means 172, 310 ... monitoring Diagnostic device, 320 ... data receiving means, 325 ... control command transmitting means, 330 ... data storage means, 340 ... device model, 350 ... device diagnosis means, 360 ... man machine interface

Claims (8)

In-facility diagnostic apparatus installed for each facility for the purpose of diagnosing the state of the apparatus installed in a remote facility, communication means for transmitting / transmitting device information related to the operating state of the apparatus from the in-facility diagnostic apparatus , and the apparatus A monitoring and diagnosing device installed in a monitoring center that receives information and monitors and diagnoses the status of devices in the facility, and the monitoring center is configured to be communicable with the remote facility via the communication means In the remote diagnosis system of
The monitoring diagnostic device has a device model that simulates the dynamic characteristics of the device, and the in-facility diagnostic device is an independent device for setting the initial state of the device model to the state of the device based on the measurement information of the device. An initial value calculating means for calculating a variable value (initial value) at a predetermined time interval; and an initial value storage means for storing and storing an initial value calculated at a predetermined time interval by the initial value calculating means for a predetermined time. Providing a state determination means for determining whether or not the state of the device is normal based on process value measurement data included in the device information related to the operation state;
The monitoring diagnostic device receives the model initial value from the in-facility diagnostic device, and performs a dynamic characteristic calculation of the device by the device model using the model initial value, and the state determination means, When it is determined that the device state is abnormal, the initial value storage means transmits the model initial value at a predetermined time before the time of occurrence of the abnormality to the monitoring and diagnosing device, and executes the device simulation using the device model. A remote diagnosis system for equipment.   The in-facility diagnostic apparatus according to claim 1, wherein the facility diagnosis apparatus stores operation data storage means for storing and storing data relating to operation of the equipment for a predetermined period of time, and uses the data stored in the operation data storage means for the equipment model. Model parameter adjustment means for calculating an adjustment parameter value for adjusting at least one of static characteristics and dynamic characteristics is provided, and the monitoring and diagnosis apparatus obtains the model initial value and the adjustment parameter value from the in-facility diagnosis apparatus. A device remote diagnosis system characterized in that the device dynamic characteristic calculation is performed using the device model using the model initial value and the adjustment parameter value. 3. The apparatus information according to claim 1, wherein the device information related to the operation state includes measurement data of a process value of the device, and the monitoring and diagnosis device uses the device model when the device state is abnormal. A remote diagnosis system for equipment having a function of identifying the cause of abnormality based on a dynamic characteristic calculation result and measurement data of the process value. In any one of claims 1 to 3, device information relating to the operating condition includes a measurement data of the process value of the equipment, the monitoring diagnostic apparatus, when the state of the equipment is abnormal, on the abnormality cause A remote diagnosis system for equipment, which has a function of predicting future ripple effects. In any one of claims 1 to 4, wherein the monitoring diagnostic apparatus has a means for transmitting a remote control signal for controlling the device remotely, said facility diagnostic apparatus remote control signal from the monitoring diagnostic apparatus And a device control unit for controlling the device by the remote control signal. 6. The monitoring diagnosis apparatus according to claim 5 , wherein the monitoring and diagnosis apparatus transmits a remote control signal for changing an operation state of the device in the remote facility to change the operation state of the device, and the same operation as the operation data measured as a result. A device remote diagnosis system characterized by having a function of diagnosing a device state based on a deviation from a calculated value of a state quantity corresponding to the operation data obtained by simulation of conditions. In Claim 6 , the said monitoring diagnostic apparatus compares the said operation data measured as a result of changing an operation condition, and the calculated value of the said state quantity corresponded to this operation data obtained by simulation of the same operation condition. A remote diagnosis system for equipment having a display function for displaying a graph on a display screen. In any one of claims 1 to 7, wherein the monitoring diagnostic apparatus that has an input means for selecting whether the time of the simulation by the equipment model, using the model initial values of any time A remote diagnosis system for equipment.

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