JPH07113594B2 - Diagnostic method for fluctuating rotating machinery - Google Patents
Diagnostic method for fluctuating rotating machineryInfo
- Publication number
- JPH07113594B2 JPH07113594B2 JP1235587A JP23558789A JPH07113594B2 JP H07113594 B2 JPH07113594 B2 JP H07113594B2 JP 1235587 A JP1235587 A JP 1235587A JP 23558789 A JP23558789 A JP 23558789A JP H07113594 B2 JPH07113594 B2 JP H07113594B2
- Authority
- JP
- Japan
- Prior art keywords
- rotating machine
- load
- abnormality
- collected
- rotation speed
- 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.)
- Expired - Lifetime
Links
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- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Description
【発明の詳細な説明】 〔産業上の利用分野〕 本発明は、回転機械の状態を表わす信号を利用して、そ
の回転機械に発生する異常の種類、部位、程度を自動的
に判定する回転機械の異常診断方法に関するものであ
る。DETAILED DESCRIPTION OF THE INVENTION [Industrial field of application] The present invention utilizes a signal representing the state of a rotating machine to automatically determine the type, part, and degree of abnormality occurring in the rotating machine. The present invention relates to a machine abnormality diagnosis method.
従来の回転機械の異常診断は、定速回転している定速回
転機械あるいは定速回転に達するまでの過渡的な回転に
おける定速回転機械に対して適用可能であった。しかし
ながら、異常診断を必要とする回転機械の中には、起動
運転完了以降の通常の運転状態において回転数や負荷の
大きさが相当範囲にわたり変動するもの（例えば、セメ
ント製造プロセスにおいては、ロータリ・キルンの駆動
装置や閉回路粉砕系におけるエアセパレータの駆動装
置）がある。このような回転機械に対して、その状態を
表わす信号を利用して異常診断を行なおうとする際、従
来の定速回転機械に対する異常診断方法では定速回転の
近傍回転でしか診断できない。一方、発電機などの起動
停止時の過渡状態における異常診断方法については、起
動停止時の過渡状態と定速運転時の定速回転状態を診断
対象としているため、起動運転完了以降の通常運転時で
回転数や負荷の大きさが相当量変動する場合と比べて、
機械の状態において両者間に大きな相異があるため、そ
の診断方法は適用できない。また、発電機などの起動停
止時の過渡状態における異常診断方法では、得られた異
常を示す徴候の進展が認められた場合、果たしてその徴
候の進展が回転機械の内部での異常の進展によるもの
か、あるいは回転数や負荷の大きさの変化によるものか
を明確に判定診断することは困難であった。The conventional abnormality diagnosis of a rotary machine can be applied to a constant speed rotary machine that is rotating at a constant speed or a constant speed rotary machine in a transient rotation until reaching a constant speed rotation. However, some rotating machines that require abnormality diagnosis have a large variation in the number of rotations and the load in a normal operating state after the start-up operation is completed (for example, in the cement manufacturing process, rotary There is a kiln drive device and an air separator drive device in a closed circuit crushing system). When attempting to perform an abnormality diagnosis on such a rotating machine by using a signal indicating the state thereof, the conventional abnormality diagnosis method for a constant speed rotating machine can perform diagnosis only in the vicinity of constant speed rotation. On the other hand, regarding the abnormality diagnosis method in the transient state at the time of starting and stopping of the generator etc., since the transient state at the time of start and stop and the constant speed rotation state at the constant speed operation are the targets of diagnosis, at the time of normal operation after completion of the start operation In comparison with the case where the number of revolutions and the size of the load fluctuate considerably,
The diagnostic method cannot be applied because there is a big difference between the two in the state of the machine. In addition, in the abnormality diagnosis method in the transient state at the time of starting and stopping the generator, etc., when the development of the sign indicating the obtained abnormality is recognized, the progress of the sign is due to the progress of the abnormality inside the rotating machine. It was difficult to make a clear diagnosis by deciding whether it is due to changes in the number of revolutions or the magnitude of load.
本発明はこのような点に鑑みてなされたものであり、そ
の目的とするところは、通常の運転状態で回転機械の回
転数や負荷の大きさが相当範囲で変動する場合におい
て、測定された信号より得られた異常徴候情報の中か
ら、回転数や負荷の大きさの変動に起因する部分と回転
機械の異常の進展に起因する部分とを分離して、回転機
械の異常の進展による徴候を明確に判断することを可能
にする変動する回転機械の診断方法を提供することにあ
る。The present invention has been made in view of such a point, and an object thereof is to measure in the case where the rotation speed and the magnitude of the load of the rotating machine fluctuate in a considerable range in a normal operating state. From the abnormal sign information obtained from the signal, the parts caused by fluctuations in the number of revolutions and the load and the parts caused by the progress of abnormalities of the rotating machine are separated, and the signs of the abnormalities of the rotating machine are shown. It is an object of the present invention to provide a method for diagnosing a rotating machine that fluctuates, which makes it possible to make a clear decision.
このような目的を達成するために本発明は、診断対象と
なる回転機械から、この回転機械が正常な時における回
転数変動範囲および負荷変動範囲内に設定された複数個
の代表点の１つの組み合わせ毎にベースラインデータを
採取し、この採取した各代表点の回転数および負荷の値
と、対応するベースラインデータとから、ベースライン
データ採取時の回転機械が正常なときにおける回転数と
負荷の大きさを変動範囲内で変動させた場合の信号の変
化を示す変動モデル係数である係数マトリクス成分Ａお
よび定数ベクトル成分ｂを設定し、異常の種類に対応す
る回転数Ｎおよび負荷Ｔにおける周波数のベースライン
スペクトルS0を、S0（N,T）＝Ax＋ｂ（ｘは回転数N,負
荷Ｔに関連する運動条件変動ベクトル）により求め、診
断時に、回転機械から診断に必要とされるデータを採取
し、異常の種類に対応した回転数Ｎおよび負荷Ｔにおけ
る周波数のスペクトルＳ（N,T）を求め、これらの求め
た値に基づいて影響分離係数ｍを、ｍ＝Ｓ（N,T）/S0
（N,T）により求め、影響分離係数ｍが予め定めた限界
値より大きくなる場合に上記回転機械を異常と判定する
ようにしたものである。In order to achieve such an object, the present invention provides one of a plurality of representative points set in a rotational speed variation range and a load variation range when a rotating machine to be diagnosed is normal. Baseline data is collected for each combination, and the rotation speed and load at the time when the rotating machine is normal at the time of collecting the baseline data are calculated from the values of the rotation speed and load at each representative point and the corresponding baseline data. Is set within the fluctuation range, the coefficient matrix component A and the constant vector component b, which are fluctuation model coefficients indicating the change in the signal, are set, and the frequency at the rotation speed N and the load T corresponding to the type of abnormality is set. The baseline spectrum S0 of S0 (N, T) = Ax + b (x is the motion condition variation vector related to the rotation speed N and the load T) is obtained from the rotating machine at the time of diagnosis. The data required for disconnection is collected, the frequency spectrum S (N, T) at the rotation speed N and the load T corresponding to the type of abnormality is obtained, and the influence separation coefficient m is calculated based on these obtained values. m = S (N, T) / S0
It is determined by (N, T), and when the influence separation coefficient m is larger than a predetermined limit value, the rotary machine is determined to be abnormal.
本発明による回転機械の異常診断方法においては、回転
数や負荷の大きさの変動による影響が分離除去されるこ
とにより、真に回転機械の異常に関係した徴候のみを得
ることを可能にする。In the abnormality diagnosing method for a rotating machine according to the present invention, it is possible to obtain only the symptom truly related to the abnormality of the rotating machine by separating and removing the influence of the variation of the rotation speed and the magnitude of the load.
以下、本発明の実施例について説明する。図は本発明に
よる変動する回転機械の診断方法の一実施例が適用され
る異常診断システムを示すブロック系統図である。図に
おいて、１はベースラインデータ採取部、２は変動モデ
ル係数設定部、３は診断データ採取部、４は運転条件変
動分離部、５は診断判定部である。Examples of the present invention will be described below. FIG. 1 is a block system diagram showing an abnormality diagnosis system to which an embodiment of a method for diagnosing a rotating machine according to the present invention is applied. In the figure, 1 is a baseline data sampling unit, 2 is a variation model coefficient setting unit, 3 is a diagnostic data sampling unit, 4 is an operating condition variation separation unit, and 5 is a diagnostic determination unit.
ベースラインデータ採取部１は、診断対象となる回転機
械自体または当該回転機械と類似する回転機械から、図
示しない信号検出端から入力される検出信号に対して例
えばフィルタリングなどの処理を行ない、アナログ／デ
ジタル変換などを行なうことにより、ベースラインのデ
ータを採取する。この場合、定常状態における回転数変
動範囲および負荷変動範囲内に各々複数個の代表点を設
定し、その代表点の１つの組合せ毎にベースラインデー
タを採取する。The baseline data sampling unit 1 performs processing such as filtering on a detection signal input from a signal detection end (not shown) from the rotating machine itself to be diagnosed or a rotating machine similar to the rotating machine, and performs analog / analog processing. Baseline data is collected by performing digital conversion. In this case, a plurality of representative points are set in the rotation speed variation range and the load variation range in the steady state, and the baseline data is collected for each combination of the representative points.
変動モデル係数設定部２は、ベースラインデータ採取部
１で採取した各代表点に対応するベースラインデータか
ら、例えば最小二乗法によって、ベースラインデータ採
取時における、回転数と負荷の大きさとを変動範囲内で
変動させた場合の信号の変化を示す変動モデルの係数を
設定する。The fluctuation model coefficient setting unit 2 changes the number of revolutions and the magnitude of the load at the time of collecting the baseline data from the baseline data corresponding to each representative point collected by the baseline data collecting unit 1 by, for example, the least square method. Set the coefficient of the fluctuation model that shows the change of the signal when it is changed within the range.
診断データ採取部３は、ベースラインデータ採取部１に
おけると同様な方法で、診断に必要なデータを採取し、
異常徴候データを算出する。The diagnostic data collection unit 3 collects data necessary for diagnosis in the same manner as in the baseline data collection unit 1,
Calculate abnormal sign data.
運転条件変動分離部４は、変動モデル係数設定部２で求
めた変動モデルを使って、運転条件である回転数や負荷
の変動の影響を分離する。The operating condition fluctuation separating unit 4 uses the fluctuation model obtained by the fluctuation model coefficient setting unit 2 to separate the influence of fluctuations in the engine speed and load, which are operating conditions.
診断判定部５は、定速回転機械の診断と同様な方法で、
異常の種類、部位、程度などを診断する。The diagnosis determination unit 5 uses the same method as the diagnosis of the constant speed rotating machine,
Diagnose the type, site, and degree of abnormality.
次に、各診断実施時に得られた徴候データから回転数変
動および負荷変動の影響を分離する方法について説明す
る。ベースラインデータ採取部１で得られ、回転機械の
異常の種類に対応する、回転数N,負荷の大きさＴにおけ
る信号のベースライン特性周波数スペクトル値Soi（ｉ
＝１〜ｎ）より成るベーススペクトルベクトルをＳ
０［N,T］とし、ｂをbi（ｉ＝１〜ｎ）より成る定数ベ
クトルとし、変動モデルが回転数Ｎと負荷の大きさＴの
２次式で表現される場合の例においては、Ａを係数行列
とし、ｘを［N,T,N2,T2］なる運転条件変動ベクトルと
すると、（１）式が成立する。Ｓ ０ ［N,T］＝Ax＋ｂ ……（１） なお、係数行列Ａの列を（２）式に示す。Next, from the symptom data obtained at the
How to separate the effects of dynamic and load fluctuations.
It Baseline data acquisition unit 1
At the number of revolutions N and the magnitude of load T that correspond to the type of abnormality
Baseline characteristic frequency spectrum value of the signal Soi(I
= 1 to n)S
0[N, T],bBiA constant constant consisting of (i = 1 to n)
And the variation model of the rotation speed N and the load magnitude T
In the case of the quadratic expression,AThe coefficient matrix
age,x[N, T, N2, T2] Operating condition fluctuation vector
Then, the expression (1) is established.S 0 [N, T] ＝Ax+b …… (1) The coefficient matrixAThe column of is shown in equation (2).
ここで、S0iはｉ番目のベースライン特性周波数スペク
トル値、Ｎは回転数、Ｔは負荷の大きさを示す量、Aij
は変動モデル係数の中の係数マトリクス成分、biは変動
モデル係数の中の定数ベクトル成分である。 Here, S 0i is the i-th baseline characteristic frequency spectrum value, N is the number of revolutions, T is a quantity indicating the magnitude of the load, and A ij
Is a coefficient matrix component in the fluctuation model coefficient, and b i is a constant vector component in the fluctuation model coefficient.
変動モデル係数設定部２において、係数行列Ａの各要素
は、ベースラインデータS0i（N1,T1）,S0i（N2,T2）,S
0i（N3,T3），・・・・,S0i（Nm,Tm）,i＝１〜ｎより、
例えば最小二乗法などの方法で求める。In the variation model coefficient setting unit 2, each element of the coefficient matrix A is the baseline data S 0i (N 1 , T 1 ), S 0i (N 2 , T 2 ), S
From 0i (N 3 , T 3 ), ..., S 0i (N m , T m ), i = 1 to n,
For example, it is obtained by a method such as the least squares method.
診断データ採取部３で得られ、回転機械の異常の種類に
対応し、回転数N,負荷の大きさＴにおける信号のスペク
トルをＳ ｉ（N,T）とする。運転条件変動分離部４にお
いて、診断実施時における回転数N,負荷の大きさＴを
（１）式に代入し、Ｓ 0i（N,T）を計算する。次に、
（２）式により影響分離係数miを計算する。 Let S i (N, T) be the spectrum of the signal, which is obtained by the diagnostic data sampling unit 3 and corresponds to the type of abnormality of the rotating machine, at the rotation speed N and the load magnitude T. In the operating condition variation separation unit 4, the rotational speed N and the load magnitude T at the time of executing the diagnosis are substituted into the equation (1), and S 0i (N, T) is calculated. next,
The influence separation coefficient m i is calculated by the equation (2).
mi≒１であれば、診断された回転機械においては異常の
徴候は増加していないものと見做すことができる。予め
定められた限界値tiに対し、mi≧tiなる関係にある場合
は、ｉに関係づけられた回転機械の異常の種類について
は、回転数変動や負荷変動を分離した形で、当該異常が
進行したと判定可能となる。以降の異常の種類、部位、
程度の診断判定は既知の回転機械の診断方法（特開昭63
−173928号公報、特開昭63−281025号公報等参照）によ
って診断可能となる。 If m i ≈1, it can be considered that there is no increase in the number of abnormal signs in the diagnosed rotating machine. When there is a relationship of m i ≧ t i with respect to a predetermined limit value t i , regarding the type of abnormality of the rotating machine associated with i, the rotational speed fluctuation and the load fluctuation are separated, It is possible to determine that the abnormality has progressed. Subsequent abnormality type, site,
The degree of diagnostic judgment is a known method for diagnosing rotating machinery (Japanese Patent Laid-Open Publication No. 63-63119
-173928, JP-A-63-281025, etc.)).
以上説明したように本発明は、回転機械が正常な時に回
転数と負荷とを相当範囲内で変動させて得られるベース
ライン徴候データ群と各診断実施時に得られる徴候デー
タ群とに基づいて回転機械の回転数および負荷の変動に
起因する影響度を抽出し、各診断実施時に得られる徴候
データから影響度を分離評価することにより回転機械の
異常を診断するようにしたことにより、回転数変動や負
荷変動による影響を分離した後、既知の定速回転機械に
対する診断方法を適用できるので、従来不可能であった
回転数や負荷が変動する回転機械に対して異常診断を可
能にする効果がある。As described above, the present invention performs rotation based on the baseline symptom data group obtained by varying the rotational speed and the load within a considerable range when the rotating machine is normal and the symptom data group obtained at the time of performing each diagnosis. Changes in the number of revolutions are detected by extracting the degree of influence caused by changes in the number of revolutions of the machine and the load, and by separately evaluating the degree of influence from the symptom data obtained at the time of each diagnosis. After separating the effects of load fluctuations and load fluctuations, a known diagnostic method for constant-speed rotating machines can be applied. is there.
図は本発明による変動する回転機械の診断方法の一実施
例が適用される異常診断システムを示すブロック系統図
である。FIG. 1 is a block system diagram showing an abnormality diagnosis system to which an embodiment of a method for diagnosing a rotating machine according to the present invention is applied.
Claims (1)
って得られる異常徴候データをもとに変動する回転機械
の異常を診断する方法において、 診断対象となる回転機械から、この回転機械が正常な時
における回転数変動範囲および負荷変動範囲内に設定さ
れた複数個の代表点の１つの組み合わせ毎にベースライ
ンデータを採取し、 この採取した各代表点の回転数および負荷の値と、対応
するベースラインデータとから、ベースラインデータ採
取時の回転機械が正常なときにおける回転数と負荷の大
きさを変動範囲内で変動させた場合の信号の変化を示す
変動モデル係数である係数マトリクス成分Ａおよび定数
ベクトル成分ｂを設定し、異常の種類に対応する回転数
Ｎおよび負荷Ｔにおける周波数のベースラインスペクト
ルS0を、 S0（N,T）＝Ax＋ｂ（ｘは回転数N,負荷Ｔに関連する運
動条件変動ベクトル） により求め、 診断時に、前記回転機械から診断に必要とされるデータ
を採取し、異常の種類に対応した回転数Ｎおよび負荷Ｔ
における周波数のスペクトルＳ（N,T）を求め、 これらの求めた値に基づいて影響分離係数ｍを、 ｍ＝Ｓ（N,T）/S0（N,T） により求め、 影響分離係数ｍが予め定めた限界値より大きくなる場合
に前記回転機械を異常と判定することを特徴とする変動
する回転機械の診断方法。1. A method for diagnosing an abnormality of a rotating machine, which fluctuates based on abnormality symptom data obtained by analyzing a detection signal representing a state of the rotating machine, wherein the rotating machine to be diagnosed is normal. The baseline data is collected for each combination of a plurality of representative points set in the rotational speed fluctuation range and the load fluctuation range at any time, and the rotation speed and the load value at each collected representative point are associated with each other. From the baseline data, the coefficient matrix component that is the fluctuation model coefficient that indicates the change in the signal when the rotation speed and load magnitude when the rotating machine is normal when the baseline data is collected are varied within the fluctuation range. A and a constant vector component b are set, and the baseline spectrum S0 of the frequency at the rotation speed N and the load T corresponding to the type of abnormality is S0 (N, T) = Ax + b (x is a rotation condition N, a motion condition fluctuation vector related to the load T), and at the time of diagnosis, data required for diagnosis is collected from the rotating machine and the rotation speed N and load corresponding to the type of abnormality are collected. T
The spectrum S (N, T) of the frequency at is calculated, and the influence separation coefficient m is calculated by m = S (N, T) / S0 (N, T) based on these calculated values. A method for diagnosing a rotating machine, wherein the rotating machine is determined to be abnormal when the value exceeds a predetermined limit value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP1235587A JPH07113594B2 (en) | 1989-09-13 | 1989-09-13 | Diagnostic method for fluctuating rotating machinery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP1235587A JPH07113594B2 (en) | 1989-09-13 | 1989-09-13 | Diagnostic method for fluctuating rotating machinery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH0399243A JPH0399243A (en) | 1991-04-24 |
| JPH07113594B2 true JPH07113594B2 (en) | 1995-12-06 |
Family
ID=16988212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP1235587A Expired - Lifetime JPH07113594B2 (en) | 1989-09-13 | 1989-09-13 | Diagnostic method for fluctuating rotating machinery |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH07113594B2 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103822758B (en) * | 2014-03-06 | 2016-05-04 | 中国石油大学(北京) | The unusual service condition inline diagnosis of heat exchanger leakage current and Selective Control method and device |
| JP7191807B2 (en) * | 2019-11-29 | 2022-12-19 | 株式会社日立製作所 | Diagnostic device and diagnostic method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS62245931A (en) * | 1986-04-18 | 1987-10-27 | Toshiba Corp | Vibration monitoring device |
-
1989
- 1989-09-13 JP JP1235587A patent/JPH07113594B2/en not_active Expired - Lifetime
Also Published As
| Publication number | Publication date |
|---|---|
| JPH0399243A (en) | 1991-04-24 |
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