JP2924242B2 - Diagnosis method for fluctuating rotating machinery - Google Patents
Diagnosis method for fluctuating rotating machineryInfo
- Publication number
- JP2924242B2 JP2924242B2 JP3062452A JP6245291A JP2924242B2 JP 2924242 B2 JP2924242 B2 JP 2924242B2 JP 3062452 A JP3062452 A JP 3062452A JP 6245291 A JP6245291 A JP 6245291A JP 2924242 B2 JP2924242 B2 JP 2924242B2
- Authority
- JP
- Japan
- Prior art keywords
- rotating machine
- virtual
- load
- range
- machine
- 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
- 238000000034 method Methods 0.000 title claims description 27
- 238000003745 diagnosis Methods 0.000 title description 22
- 230000005856 abnormality Effects 0.000 claims description 26
- 238000004458 analytical method Methods 0.000 claims description 19
- 238000001514 detection method Methods 0.000 claims description 16
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 208000024891 symptom Diseases 0.000 claims description 8
- 238000001228 spectrum Methods 0.000 description 15
- 239000011159 matrix material Substances 0.000 description 12
- 238000004364 calculation method Methods 0.000 description 4
- 238000002405 diagnostic procedure Methods 0.000 description 3
- 230000001052 transient effect Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010298 pulverizing process Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P5/00—Arrangements specially adapted for regulating or controlling the speed or torque of two or more electric motors
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Description
【０００１】[0001]
【産業上の利用分野】本発明は、回転機械の状態を表す
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ようにした変動する回転
機械の診断方法に関するものである。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention uses a signal indicating the state of a rotating machine to detect the type of abnormality occurring in the rotating machine.
The present invention relates to a method of diagnosing a fluctuating rotating machine in which a part and a degree are automatically determined.
【０００２】[0002]
【従来の技術】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
過度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、
その状態を表す信号を利用して異常診断を行おうとする
場合には、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。2. Description of the Related Art Conventionally, abnormality diagnosis of a rotating machine is applicable to a constant-speed rotating machine rotating at a constant speed or a constant-speed rotating machine in an excessive rotation until reaching a constant-speed rotation. However, among the rotating machines that require abnormality diagnosis, the number of rotations and the magnitude of the load fluctuate over a considerable range in a normal operation state after the completion of the start-up operation (for example, in a cement manufacturing process, a rotary / Kiln driving device and air separator driving device in closed circuit pulverizing system). For such a rotating machine,
When an abnormality diagnosis is to be performed using a signal representing the state, the conventional abnormality diagnosis method for a constant-speed rotating machine can perform diagnosis only at a rotation near the constant-speed rotation.
【０００３】一方、発電機等の起動停止時の過度状態に
おける異常診断方法については、起動停止時の過度状態
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違があるため、その診断方法は適用
できない。また、発電機等の起動停止時の過度状態にお
ける異常診断方法では、得られた異常を示す徴候に進展
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なのか、あるいは回転
数や負荷の大きさの変化によるものなのかを明確に判定
診断することは困難である。[0003] On the other hand, regarding a method of diagnosing abnormalities in the transient state when the generator or the like is stopped, the transient state when the generator is stopped and the constant-speed rotation state during the constant-speed operation are to be diagnosed. The diagnostic method cannot be applied because there is a great difference between the two in the state of the machine as compared to the case where the rotation speed and the magnitude of the load fluctuate by a considerable amount during normal operation. Also, in the method of diagnosing abnormalities in the transient state at the time of starting and stopping the generator, etc., if progress is observed in the signs indicating the obtained abnormalities, the progress of the signs is due to the progress of abnormalities inside the rotating machine It is difficult to clearly determine and diagnose whether this is due to changes in the number of revolutions or the load.
【０００４】また、他に、本出願人の別途出願による特
願平１−２３５５８７号の変動する回転機械の診断方法
がある。この方法は、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して回転機械の異常を診断するよう
にしたものであり、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
用するようにした方法である。In addition, there is another method for diagnosing a fluctuating rotating machine disclosed in Japanese Patent Application No. 1-235587 filed by the present applicant. This method is based on a baseline sign data group obtained by varying the rotation speed and load within a considerable range when the rotating machine is normal and a sign data group obtained at the time of each diagnosis, the rotation speed of the rotating machine and It extracts the degree of influence due to load fluctuations and separates and evaluates the degree of influence from the symptom data obtained at the time of each diagnosis to diagnose abnormalities in rotating machinery. And then apply a diagnosis method for a known constant-speed rotating machine.
【０００５】[0005]
【発明が解決しようとする課題】上述した変動する回転
機械の診断方法では、各診断実施時に得られる徴候デー
タから回転数および負荷の変動に起因する影響度を分離
評価するための変動モデルを作成する際に、回転機械の
異常に対応する特定スペクトルの個数が少ない場合や変
動モデルが単純な関数の場合は実用上その適用が可能で
あるが、特定スペクトルの個数が多い場合や変動モデル
が複雑な場合は、そのモデル作成のために多大な時間や
労力を要し、現実的に変動モデルの作成が困難になる等
の欠点があった。従って本発明は、上述したような変動
モデル作成に要する多大な労力を省略して簡便かつ的確
な診断方法を提供することを目的とする。In the method for diagnosing a fluctuating rotating machine described above, a fluctuation model for separating and evaluating the degree of influence caused by fluctuations in the rotational speed and load from symptom data obtained at the time of each diagnosis is created. When the number of specific spectra corresponding to abnormalities in the rotating machine is small or when the variation model is a simple function, it can be applied practically, but when the number of specific spectra is large or the variation model is complicated. In such a case, there is a drawback that it takes a lot of time and effort to create the model, and it becomes difficult to create a fluctuation model in reality. Accordingly, it is an object of the present invention to provide a simple and accurate diagnosis method without a large amount of labor required for creating a fluctuation model as described above.
【０００６】[0006]
【課題を解決するための手段】上述の目的を達成するた
めに本発明は、回転機械の状態を表す検出信号の解析に
よって得られる異常徴候データに基づき回転機械の異常
を診断する方法において、通常の運転状態で上記回転機
械の回転数及び負荷の大きさが相当範囲で変動する場合
に、上記検出信号に及ぼす影響が軽微な範囲で、かつ一
定時間内に運転可能である単一変動範囲を複数組設定す
ることにより、１台の変動する上記回転機械を、それぞ
れ異なる単一変動範囲を有する複数の仮想回転機械の集
合体として取り扱い、各仮想回転機械に対し、定回転・
定負荷回転機械の異常診断と同等の方法を適用するよう
にした方法である。SUMMARY OF THE INVENTION In order to achieve the above object, the present invention relates to a method of diagnosing abnormality of a rotating machine based on abnormality sign data obtained by analyzing a detection signal representing the state of the rotating machine. of when the rotational speed and the magnitude of the load of the rotary machine fluctuates corresponding range operating conditions, the influence is slight extent on the detection signal, and a single variable range can be operated within a predetermined time by plurality of sets setting, the rotating machine varying one, it
Re different single variation range handled as a plurality of sets of virtual rotary machine having, for each virtual rotary machine, constant speed,
This is a method in which a method equivalent to the abnormality diagnosis of a constant load rotating machine is applied.
【０００７】[0007]
【作用】本発明においては、１台の変動する回転機械を
複数組の単一変動範囲を有する仮想回転機械の集合体と
して取り扱うことにより、定回転・定負荷回転機械と同
等の異常診断方法を適用できるようにしたもので、従来
の変動する回転機械の診断方法に比して、簡便かつ的確
な適用が可能となる。According to the present invention, an abnormality diagnosis method equivalent to a constant rotation / constant load rotation machine is provided by treating a single rotating machine as a set of virtual rotating machines having a plurality of single variable ranges. The present invention can be applied, and simple and accurate application becomes possible as compared with a conventional method of diagnosing a fluctuating rotating machine.
【０００８】[0008]
【実施例】以下、図面を用いて本発明の実施例を詳細に
説明する。図１は、本発明に係る変動する回転機械の診
断方法を適用したシステムの一実施例を示すブロック系
統図である。同図において、１は変動範囲・設備諸元入
力部、２は変動範囲・分析条件設定部、３は変動範囲・
分析条件記憶部、４はプロセス情報検出部、５は変動範
囲探索部、６は振動検出部、７は増幅器である。また、
８は信号処理部、９はＡ／Ｄ変換器、１０はディジタル
周波数分析部、１１は時系列データ演算部、１２は仮想
回転機械別時系列特徴マトリックス記憶部、１３は仮想
回転機械別時系列異常徴候マトリックス形成部、１４は
診断判定部、１５は初期値スペクトル記憶部、１６は制
御部である。Embodiments of the present invention will be described below in detail with reference to the drawings. FIG. 1 is a block diagram showing an embodiment of a system to which a method for diagnosing a fluctuating rotating machine according to the present invention is applied. In the figure, 1 is a fluctuation range / equipment specification input section, 2 is a fluctuation range / analysis condition setting section, 3 is a fluctuation range /
An analysis condition storage unit, 4 is a process information detection unit, 5 is a fluctuation range search unit, 6 is a vibration detection unit, and 7 is an amplifier. Also,
8 is a signal processing unit, 9 is an A / D converter, 10 is a digital frequency analysis unit, 11 is a time series data operation unit, 12 is a time series feature matrix storage unit for each virtual rotating machine, and 13 is a time series for each virtual rotating machine. An abnormal sign matrix forming unit, 14 is a diagnosis determining unit, 15 is an initial value spectrum storage unit, and 16 is a control unit.
【０００９】図１において、変動範囲・設備諸元入力部
１は、診断の対象となる変動する回転機械を、変動範囲
を複数個設定することにより、１台の変動する回転機械
を複数個の単一変動範囲を有する仮想回転機械の集合体
として取り扱いができるように、回転数および負荷等の
変動範囲を複数組（最小の場合は１組）入力し、各変動
範囲に対応する分析条件設定に必要な回転機械の構成要
素、即ち減速機の歯車の枚数等の回転要素や軸受の仕様
等で示される設備諸元を入力する。変動範囲・分析条件
設定部２は、変動範囲・設備諸元入力部１において入力
されたデータをもとに、複数組の変動範囲毎に回転数お
よび負荷等の上下限値の変動範囲とその振動範囲毎の振
動，回転数，負荷情報等の検出信号の種類と検出位置，
信号処理の種類，周波数分析周波数帯域，回転機械の各
種の異常に対応する周波数等の信号分析を自動的に実施
するための条件・方法を規定するデータを設定し、変動
範囲・分析条件記憶部３においてこれらのデータは記憶
される。また、プロセス情報検出部４では、回転機械か
ら発生する回転数や負荷情報等を検出する。そして、変
動範囲探索部５は、プロセス情報検出部４で検出された
回転数や負荷データ等をもとに、変動する回転機械の複
数組ある変動範囲に対応する仮想回転機械のうちこれが
どの変動範囲に対応する仮想回転機械に該当するかを探
索し、その変動範囲に対応する仮想回転機械を決定す
る。また、回転数補正用の軸回転数も検出してこれを記
憶する。変動範囲探索部５で決定された変動範囲に対応
する仮想回転機械の分析条件にしたがって振動検出部６
は、回転機械から発生する振動を検出し、増幅器７でそ
の振動信号を増幅し、信号処理部８でフィルタリング等
の信号処理を行った後、Ａ／Ｄ変換器９によりアナログ
／ディジタル変換を行う。また、ディジタル周波数分析
器１０はその周波数分析を行う。In FIG. 1, a variable range / equipment specification input unit 1 sets a plurality of variable rotating machines to be diagnosed by setting a plurality of variable ranges so that one variable rotating machine can be divided into a plurality of variable rotating machines. A plurality of sets (one set at minimum) of fluctuation ranges such as rotation speed and load are input so that they can be handled as a set of virtual rotating machines having a single fluctuation range, and analysis condition setting corresponding to each fluctuation range Of the rotating machine, ie, the rotating elements such as the number of gears of the speed reducer and the equipment specifications indicated by the specifications of the bearings are input. The fluctuation range / analysis condition setting unit 2 uses the data input in the fluctuation range / equipment specification input unit 1 to determine the fluctuation range of the upper and lower limit values such as the rotation speed and the load for each of a plurality of fluctuation ranges. The types and detection positions of detection signals such as vibration, rotation speed, and load information for each vibration range,
Set the data that specifies the conditions and methods for automatically performing signal analysis, such as the type of signal processing, frequency analysis frequency band, and frequency corresponding to various abnormalities of the rotating machine, and store the fluctuation range and analysis condition storage unit. At 3 these data are stored. Further, the process information detecting section 4 detects the number of rotations and load information generated from the rotating machine. Then, the fluctuation range search unit 5 determines, based on the rotation speed, load data, and the like detected by the process information detection unit 4, which of the virtual rotating machines corresponding to a plurality of fluctuation ranges of the rotating machine fluctuates. The virtual rotating machine corresponding to the range is searched for, and a virtual rotating machine corresponding to the fluctuation range is determined. Further, a shaft rotation speed for rotation speed correction is also detected and stored. The vibration detection unit 6 according to the analysis condition of the virtual rotating machine corresponding to the fluctuation range determined by the fluctuation range search unit 5
Detects the vibration generated from the rotating machine, amplifies the vibration signal by the amplifier 7, performs signal processing such as filtering by the signal processing unit 8, and performs analog / digital conversion by the A / D converter 9. . The digital frequency analyzer 10 performs the frequency analysis.
【００１０】初期値スペクトル記憶部１５には、ベース
ラインデータとしての初期値スペクトルが、予め、変動
範囲毎に対応する仮想回転機械毎に記憶されており、変
動範囲探索部５で決定された変動範囲に対応した仮想回
転機械の初期値スペクトルが初期値スペクトル記憶部１
５から時系列データ演算部１１に送出される。時系列デ
ータ演算部１１は、ディジタル周波数分析部１０で周波
数分析されたデータを得て、変動範囲探索部５で決定さ
れた変動範囲に対応する仮想回転機械の各特定周波数に
対して変動範囲探索部５で記憶している回転数を基に、
決定した変動範囲内での回転数変動による回転数補正を
行い、各特定周波数毎のスペクトル成分値を求め、決定
した変動範囲毎に対応する仮想回転機械の各種異常に対
応する時系列的特徴量を演算して仮想回転機械別時系列
特徴マトリックス記憶部１２へ送出しこれらのデータを
記憶させる。The initial value spectrum storage unit 15 stores in advance an initial value spectrum as baseline data for each virtual rotating machine corresponding to each fluctuation range. The initial value spectrum of the virtual rotating machine corresponding to the range is stored in the initial value spectrum storage unit 1.
5 to the time-series data calculation unit 11. The time-series data calculation unit 11 obtains the data subjected to the frequency analysis by the digital frequency analysis unit 10 and searches the fluctuation range for each specific frequency of the virtual rotating machine corresponding to the fluctuation range determined by the fluctuation range search unit 5. Based on the number of revolutions stored in the part 5,
Performs rotation speed correction based on rotation speed fluctuations within the determined fluctuation range, finds spectrum component values for each specific frequency, and time-series feature values corresponding to various abnormalities of the virtual rotating machine corresponding to each determined fluctuation range And sends it to the time series feature matrix storage unit 12 for each virtual rotating machine to store these data.
【００１１】仮想回転機械別時系列異常徴候マトリック
ス形成部１３は、変動範囲・分析条件記憶部３に記憶さ
れている決定された変動範囲に対応する仮想回転機械の
基準値群を使い、異常徴候が一定水準に進展したデータ
のみを抽出し、その抽出されたデータに異常の種類，部
位，異常に対応するスペクトル周波数とその相互関係情
報を付加して、変動範囲探索部５で決定された変動範囲
に対応する仮想回転機械に対応する異常徴候マトリック
スを形成し、診断判定部１４へ送出することにより定回
転回転機械と同様な方法で異常の種類，部位，程度や残
存寿命等を診断判定させ表示出力させる。また、制御部
１６は上記の一連の診断プロセスを自動的に実行制御す
る機能を有している。The time series abnormal symptom matrix forming unit 13 for each virtual rotating machine uses the reference value group of the virtual rotating machine corresponding to the determined fluctuation range stored in the fluctuation range / analysis condition storage unit 3 to generate an abnormal symptom. Extracts only the data that has evolved to a certain level, adds the type of abnormality, the site, the spectrum frequency corresponding to the abnormality and its correlation information to the extracted data, and determines the fluctuation determined by the fluctuation range search unit 5. An abnormal symptom matrix corresponding to the virtual rotating machine corresponding to the range is formed, and is sent to the diagnosis determining unit 14 to diagnose and determine the type, location, degree, remaining life, and the like of the abnormality in the same manner as the constant rotating machine. Display output. Further, the control unit 16 has a function of automatically executing and controlling the above-described series of diagnostic processes.
【００１２】次に、各回転機械に対し回転数や負荷等の
変動が回転機械の状態を表す検出信号に及ぼす影響が軽
微な範囲で、かつ、一定時間内に運転可能である変動範
囲を複数組用意することにより、１台の変動する回転機
械を複数組の単一変動範囲を有する仮想回転機械の集合
体として取り扱い、この結果、各仮想回転機械に対し定
回転・定負荷回転機械と同等の診断が可能となるような
異常診断の方法について詳述する。変動範囲探索部５
は、各診断実施時毎に、プロセス情報検出部４で検出さ
れた診断対象機械の回転数と負荷データ等を基に、変動
範囲・分析条件記憶部３に記憶されているデータを参照
してどの変動範囲に該当するか決定し、その決定した変
動範囲に対応する分析条件にしたがって一連のデータを
取り扱い、一方、時系列データ演算部１１では定回転・
定負荷回転機械と同等の診断方法が適用可能なように、
変動範囲探索部５で決定された変動範囲に対応するデー
タを演算して仮想回転機械別時系列特徴マトリックス記
憶部１２へ送出し格納させる。Next, for each rotary machine, a plurality of fluctuation ranges within which the fluctuation of the number of revolutions, load, and the like exerts little influence on the detection signal indicating the state of the rotary machine and which can be operated within a certain period of time. By preparing a set, one variable rotating machine is treated as a set of virtual rotating machines having a plurality of single variable ranges, and as a result, each virtual rotating machine is equivalent to a constant rotating / constant rotating machine. A method of diagnosing abnormalities that enables the diagnosis of the above will be described in detail. Change range search unit 5
Refers to the data stored in the fluctuation range / analysis condition storage unit 3 based on the rotation speed and load data of the diagnosis target machine detected by the process information detection unit 4 every time each diagnosis is performed. It decides which variation range falls, and handles a series of data according to the analysis conditions corresponding to the determined variation range.
To be able to apply the same diagnostic method as a constant load rotating machine,
Data corresponding to the variation range determined by the variation range search unit 5 is calculated and sent to the virtual rotary machine-specific time-series feature matrix storage unit 12 for storage.
【００１３】表１は、変動範囲毎に対応して記憶された
仮想回転機械の仮想回転機械別特徴マトリックスの例で
ある。Table 1 is an example of a virtual rotating machine-specific feature matrix of virtual rotating machines stored corresponding to each variation range.
【００１４】[0014]
【表１】 [Table 1]
【００１５】ここで、Ｓ0 （ｎ，ｉ）は変動範囲毎に対
応する仮想回転機械毎の各特定周波数と対応する初期値
スペクトル、Ｒ（ｎ，ｉ，ｍ）は変動範囲毎の各特定周
波数に対応する時系列的相対スペクトル比である。そし
て、Ｒ（ｎ，ｉ，ｍ）の演算は、変動範囲探索部５で決
定された変動範囲に対応する仮想回転機械の各特定周波
数毎に、時系列データ演算部１１において、回転数補正
して得た各特定周波数のスペクトル成分値とその変動範
囲に対応する仮想回転機械の特定周波数の初期スペクト
ルＳ0 （ｎ，ｉ）とを基に演算されるため、回転数や負
荷等の変動による影響が軽微な時系列的相対スペクトル
比が演算できる。なお、ｎは変動範囲に対応する仮想回
転機械の仮想回転機械別時系列特徴マトリックス、ｉは
特定周波数、ｊは時系列を示す。Here, S 0 (n, i) is an initial value spectrum corresponding to each specific frequency for each virtual rotating machine corresponding to each fluctuation range, and R (n, i, m) is each specification spectrum for each fluctuation range. It is a time-series relative spectral ratio corresponding to a frequency. The calculation of R (n, i, m) is performed by the time-series data calculator 11 for each specific frequency of the virtual rotating machine corresponding to the fluctuation range determined by the fluctuation range search unit 5 by performing the rotation correction. It is calculated based on the spectrum component values of each specific frequency obtained in the above and the initial spectrum S 0 (n, i) of the specific frequency of the virtual rotating machine corresponding to the fluctuation range thereof, so that it is affected by the fluctuation of the rotation speed, load and the like. A time-series relative spectral ratio with little effect can be calculated. Here, n is a time-series feature matrix for each virtual rotating machine of the virtual rotating machine corresponding to the fluctuation range, i is a specific frequency, and j is a time series.
【００１６】このように、時系列データ演算部１１にお
いては、変動範囲探索部５で決定された変動範囲に対応
する仮想回転機械の各特定周波数の時系列的相対スペク
トル比を演算し、仮想回転機械別時系列特徴マトリック
ス記憶部１２へ記憶させ、仮想回転機械別時系列異常徴
候マトリックス形成することで、回転数や負荷等の変動
に起因する回転機械の状態を表す検出信号に及ぼす影響
が軽微な場合の異常進行の判定が可能となる。そして、
以降の異常の種類，部位，程度の診断判定は、例えば本
出願人の別途出願による特願平１−２３５５８６号の回
転機械の異常診断方法によってその診断が可能となる。As described above, the time series data calculation section 11 calculates the time series relative spectrum ratio of each specific frequency of the virtual rotating machine corresponding to the fluctuation range determined by the fluctuation range search section 5, and calculates the virtual rotation speed. By storing it in the machine-specific time-series feature matrix storage unit 12 and forming the virtual rotating machine-specific time-series abnormal symptom matrix, the influence on the detection signal indicating the state of the rotating machine due to fluctuations in the rotation speed, load, and the like is minimal. In such a case, it is possible to determine the abnormal progress. And
Subsequent diagnosis and determination of the type, location, and degree of abnormality can be made by, for example, a rotating machine abnormality diagnosis method disclosed in Japanese Patent Application No. 1-235586 filed by the present applicant.
【００１７】[0017]
【発明の効果】以上説明したように本発明では、通常の
運転状態で回転機械の回転数や負荷等の大きさが相当範
囲で変動する場合に、各回転機械に対し回転数や負荷等
の変動が回転機械の状態を表す検出信号に及ぼす影響が
軽微な範囲で、かつ、一定時間内に運転可能である変動
範囲を複数個設定することにより、１台の変動する回転
機械を複数個の単一変動範囲を有する仮想回転機械の集
合体として取り扱うようにしたので、各仮想回転機械に
対して定回転・定負荷回転機械の異常診断と同等の方法
が適用可能となり、従来の変動する回転機械への診断適
用に際し、制約無しに正確かつ簡便にその適用が可能と
なる。As described above, according to the present invention, when the rotation speed, load, etc. of a rotating machine fluctuates within a considerable range in a normal operating state, the rotation speed, load, etc. By setting a plurality of fluctuation ranges within which the fluctuation has a small effect on the detection signal indicating the state of the rotating machine and which can be operated within a fixed time, one fluctuating rotating machine can be divided into a plurality of rotation machines. Since it is handled as a set of virtual rotating machines with a single fluctuation range, the same method as that for diagnosis of abnormalities of constant rotation and constant load rotating machines can be applied to each virtual rotating machine. In applying the diagnostics to the machine, it is possible to apply the diagnostics accurately and simply without any restrictions.
【図１】本発明に係る変動する回転機械の診断方法を適
用したシステムの一実施例を示すブロック系統図であ
る。FIG. 1 is a block diagram showing an embodiment of a system to which a method for diagnosing a fluctuating rotating machine according to the present invention is applied.
１ 変動範囲・設備諸元入力部 ２ 変動範囲・分析条件設定部 ３ 変動範囲・分析条件記憶部 ４ プロセス情報検出部 ５ 変動範囲探索部 ６ 振動検出部 ７ 増幅器 ８ 信号処理部 ９ Ａ／Ｄ変換器 １０ ディジタル周波数分析部 １１ 時系列データ演算部 １２ 仮想回転機械別時系列特徴マトリックス記憶部 １３ 仮想回転機械別時系列異常徴候マトリックス形
成部 １４ 診断判定部 １５ 初期値スペクトル記憶部 １６ 制御部DESCRIPTION OF SYMBOLS 1 Change range and equipment specification input part 2 Change range and analysis condition setting part 3 Change range and analysis condition storage part 4 Process information detection part 5 Change range search part 6 Vibration detection part 7 Amplifier 8 Signal processing part 9 A / D conversion 10 Digital frequency analysis unit 11 Time series data operation unit 12 Time series feature matrix storage unit for each virtual rotating machine 13 Time series abnormal symptom matrix formation unit for each virtual rotating machine 14 Diagnosis judgment unit 15 Initial value spectrum storage unit 16 Control unit
フロントページの続き (56)参考文献 特開 昭57−1926（ＪＰ，Ａ) 特開 昭59−63529（ＪＰ，Ａ) (58)調査した分野(Int.Cl.6，ＤＢ名) G01M 19/00 H02K 11/00 Continuation of the front page (56) References JP-A-57-1926 (JP, A) JP-A-59-63529 (JP, A) (58) Fields investigated (Int. Cl. 6 , DB name) G01M 19 / 00 H02K 11/00
Claims (1)
よって得られる異常徴候データに基づき前記回転機械の
異常を診断する方法において、 通常の運転状態で前記回転機械の回転数及び負荷の大き
さが相当範囲で変動する場合に、前記検出信号に及ぼす
影響が軽微な範囲で、かつ一定時間内に運転可能である
単一変動範囲を複数組設定することにより、１台の変動
する前記回転機械を、それぞれ異なる単一変動範囲を有
する複数の仮想回転機械の集合体として取り扱い、各仮
想回転機械に対し、定回転・定負荷回転機械の異常診断
と同等に異常の診断が行えるようにしたことを特徴とす
る変動する回転機械の診断方法。1. A method of diagnosing an abnormality of the rotary machine based on the abnormal symptom data obtained by the analysis of the detection signal indicating the state of a rotary machine, the rotational speed and magnitude of the load of the rotary machine in normal operating conditions If There that varies corresponding range can be operated in the with minor extent influence on the detection signal, and a predetermined time
By plural sets configuring a single variation range, the rotating machine varying one handle as an assembly of a plurality of virtual rotary machine having a different single variation range respectively, for each virtual rotary machine, constant rotation A method for diagnosing a fluctuating rotating machine, wherein an abnormality can be diagnosed in the same manner as that for a constant load rotating machine.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP3062452A JP2924242B2 (en) | 1991-03-05 | 1991-03-05 | Diagnosis method for fluctuating rotating machinery |
| KR1019920003544A KR960000803B1 (en) | 1991-03-05 | 1992-03-04 | The method for checking a rotary machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP3062452A JP2924242B2 (en) | 1991-03-05 | 1991-03-05 | Diagnosis method for fluctuating rotating machinery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH04276537A JPH04276537A (en) | 1992-10-01 |
| JP2924242B2 true JP2924242B2 (en) | 1999-07-26 |
Family
ID=13200614
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP3062452A Expired - Lifetime JP2924242B2 (en) | 1991-03-05 | 1991-03-05 | Diagnosis method for fluctuating rotating machinery |
Country Status (2)
| Country | Link |
|---|---|
| JP (1) | JP2924242B2 (en) |
| KR (1) | KR960000803B1 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6525144B1 (en) | 1997-08-19 | 2003-02-25 | Nippon Zeon Co., Ltd. | Norbornene polymer and production process |
| KR20010026772A (en) * | 1999-09-08 | 2001-04-06 | 이구택 | Monitoring and diagnosis method for facility with variable operating condition |
-
1991
- 1991-03-05 JP JP3062452A patent/JP2924242B2/en not_active Expired - Lifetime
-
1992
- 1992-03-04 KR KR1019920003544A patent/KR960000803B1/en not_active IP Right Cessation
Also Published As
| Publication number | Publication date |
|---|---|
| KR920019058A (en) | 1992-10-22 |
| KR960000803B1 (en) | 1996-01-12 |
| JPH04276537A (en) | 1992-10-01 |
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