JP2010203929A - Abnormality diagnostic system in mechanical equipment - Google Patents
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Abstract
Description
本発明は、機械設備の異常診断技術に関し、特に回転機械から得られる振動データを解析することにより初期の微小な異常を早期に漏れなく検知する、機械設備における異常診断システムに関するものである。 The present invention relates to an abnormality diagnosis technique for mechanical equipment, and more particularly to an abnormality diagnosis system for mechanical equipment that detects early minute abnormalities without omission by analyzing vibration data obtained from a rotating machine.
回転機械の稼動状態の良否を判定する従来技術としては、次のような技術が知られている。先ず第1の技術として、回転機器に発生する振動変位、振動速度、振動加速度等を計測し、その計測値から、振動の最大値や実効値などを求め、それらの値を基準値と比較することにより、正常、注意、危険の判定を行う技術を挙げることができる。 The following techniques are known as conventional techniques for determining whether the operating state of the rotating machine is good or bad. First, as a first technique, vibration displacement, vibration speed, vibration acceleration, etc. occurring in rotating equipment are measured, and the maximum value or effective value of vibration is obtained from the measured values, and these values are compared with reference values. Therefore, it is possible to list technologies for determining normality, caution, and danger.
次に上記技術を発展させた第2の技術として、例えば特許文献１で開示された技術がある。これは上記基準値を回転機器の状態等により自動更新する方法である。 Next, as a second technique developed from the above technique, for example, there is a technique disclosed in Patent Document 1. This is a method of automatically updating the reference value according to the state of the rotating device or the like.
さらに第3の技術として、例えば特許文献２で開示された技術がある。これは、周波数解析により、異常状態に起因する基本周波数成分とその自然数倍の周波数成分の大きさを比較する技術である。 Further, as a third technique, for example, there is a technique disclosed in Patent Document 2. This is a technique for comparing the magnitude of a fundamental frequency component caused by an abnormal state and a frequency component that is a natural number multiple by frequency analysis.
しかしながら、上述した背景技術には次のような問題がある。先ず第1の技術には、振動を計測するセンサの取付け対象設備および取付け位置、さらに計測時における機械設備の状態等により、振動レベルやノイズレベルが変わり、適切な基準値を与えるのが難しいという問題がある。 However, the background art described above has the following problems. First of all, the first technology says that it is difficult to give an appropriate reference value because the vibration level and noise level change depending on the equipment and mounting position of the sensor that measures vibration and the state of the mechanical equipment at the time of measurement. There's a problem.
また第2の技術は、上記問題点を解決するために考案された技術であるが、緩やかに振動レベルが上昇していく場合や、ノイズの出現の仕方に変化がある場合などには、適切な基準値が得られにくいという問題がある。 The second technique is a technique devised to solve the above problems, but it is appropriate when the vibration level gradually rises or there is a change in the appearance of noise. There is a problem that it is difficult to obtain a standard value.
さらに第3の技術には、周波数解析を行うため、計算負荷が高く、特に監視対象が数千点に及ぶような巨大なシステムで実現するにはコストが非常に高くなるという問題がある。 Furthermore, the third technique has a problem that the calculation load is high because frequency analysis is performed, and the cost is extremely high in order to realize it in a huge system in which thousands of monitoring targets are in particular.
本発明は、上記事情に鑑みてなされたもので、安価な導入、メンテナンスにかかる労力の大幅削減、かつ、初期の微小な異常を漏れなく早期検知ができる、機械設備における異常診断システムを提供することを目的とする。 The present invention has been made in view of the above circumstances, and provides an abnormality diagnosis system in mechanical equipment that can be inexpensively introduced, greatly reduced labor required for maintenance, and early detection of an initial minute abnormality without omission. For the purpose.
本発明の請求項１に係る発明は、機械設備に設置したセンサで測定した信号を分析することにより、機械設備の異常を検知する機械設備における異常診断システムであって、測定した信号から、平均値および標準偏差を逐次求め、該平均値および標準偏差を用いて逐次正規化値を算出する、逐次正規化値算出部と、該逐次正規化値算出部で算出された逐次正規化値に基づいて機械設備の異常を検知する異常判定部とを備えることを特徴とする機械設備における異常診断システムである。 The invention according to claim 1 of the present invention is an abnormality diagnosis system in a mechanical facility that detects an abnormality in the mechanical facility by analyzing a signal measured by a sensor installed in the mechanical facility. A sequential normalization value calculation unit that sequentially calculates a value and a standard deviation, and calculates a normalization value using the average value and the standard deviation, and a sequential normalization value calculated by the sequential normalization value calculation unit An abnormality diagnosis system for mechanical equipment, comprising an abnormality determination unit that detects an abnormality of the mechanical equipment.
また本発明の請求項２に係る発明は、請求項１に記載の機械設備における異常診断システムにおいて、前記信号のトレンド成分を抽出するトレンド成分抽出部を備え、抽出したトレンド成分に基づいて、前記逐次正規化値算出部での処理を行うことを特徴とする機械設備における異常診断システムである。 The invention according to claim 2 of the present invention is the abnormality diagnosis system for mechanical equipment according to claim 1, further comprising a trend component extraction unit that extracts the trend component of the signal, and based on the extracted trend component, An abnormality diagnosis system for mechanical equipment, characterized by performing processing in a sequential normalized value calculation unit.
本発明は、測定した信号から平均値および分散値を逐次求め、さらに逐次正規化値を算出し、この逐次正規化値に基づいて機械設備の異常を検知するようにしたので、既存のシステムに安価に導入でき、かつ、初期の微小な異常を漏れなく早期検知できるようになる。また、大量にある監視対象に対して同一の上限値を設定することができ、メンテナンスにかける労力を大幅に削減することが可能となる。 In the present invention, the average value and the variance value are sequentially obtained from the measured signal, the normalized value is further calculated, and the abnormality of the mechanical equipment is detected based on the sequentially normalized value. It can be introduced at a low cost and can detect early minute abnormalities without omission. Further, the same upper limit value can be set for a large number of monitoring targets, and the labor required for maintenance can be greatly reduced.
以下、図面および数式を参照しながら、本発明を具体的に説明してゆく。図２は、本発明で異常を早期に検知したい代表的事例を説明する図である。この図で示す各データは、回転機器の軸受部に設置した振動計で計測された加速度の実効値の時間推移データであり、（ａ）は緩やかに変化するケース、（ｂ）は急激に変化するケース、および（ｃ）はノイズの出現が変化するケースをそれぞれ示している。 Hereinafter, the present invention will be specifically described with reference to the drawings and mathematical expressions. FIG. 2 is a diagram for explaining a typical case in which an abnormality is desired to be detected early in the present invention. Each data shown in this figure is the time transition data of the effective value of the acceleration measured by the vibrometer installed on the bearing portion of the rotating device, where (a) shows a slowly changing case and (b) shows a sudden change. And (c) show cases where the appearance of noise changes.
従来の異常診断システムでは、注意レベル、危険レベルの２つの上限値が設定されおり、これら上限値を超えると警報が出力される。しかし、図２に示した代表例は、いずれも最終的には、図中にそれぞれ矢印で「損傷」と示した時点で機器損傷に至った事例であり、さらに機器損傷に至る前にある、「予知したい時点」として図中に矢印で示したタイミングで異常の検知ができていれば、潤滑油や部品の交換をすることにより、機器の損傷を避けることができた事例である。 In the conventional abnormality diagnosis system, two upper limit values of a caution level and a danger level are set, and an alarm is output when these upper limit values are exceeded. However, all of the representative examples shown in FIG. 2 are cases in which device damage is finally caused at the time indicated as “damage” by arrows in the figure, and further before device damage occurs. If an abnormality can be detected at the timing indicated by the arrow in the figure as the “time to predict”, this is an example in which damage to the equipment could be avoided by replacing the lubricating oil and parts.
図２に示す時系列グラフを概観すると、矢印位置での検知は一見簡単そうに見えるものの、これを数値化した指標で自動的に検知させようとすると容易なことではない。その理由は、設備対象別に得られる信号レベルが異なること、しかもその対象の数が工場の場合には何千と大量であること、また、正常範囲から異常値への変化が急激なケースもあれば、連続的で明確な境界をつけにくいケースなど種々のパターンがあることなどによる。 When looking at the time series graph shown in FIG. 2, the detection at the arrow position seems to be easy at first glance, but it is not easy to automatically detect this with a numerical index. The reason is that the signal level obtained for each facility object is different, and that the number of objects is thousands when the factory is large, and the change from the normal range to the abnormal value may be abrupt. This is because there are various patterns such as cases where it is difficult to make a continuous and clear boundary.
このような場合、従来の閾値で検知する方法では限界がある。変化の特徴をうまく表現する特徴量を抽出する方法も考えられるものの、変化のパターンは多数あるため、これもすべてのパターンに対して適切な特徴量を用意するのは難しいと言わざるを得ない。また、漏れのあるパターンに対しては見逃しの危険も生じてしまう。 In such a case, there is a limit to the conventional detection method using a threshold value. Although it is possible to extract features that express the characteristics of changes well, there are many changes patterns, so it must be said that it is difficult to prepare appropriate features for all patterns. . In addition, there is a risk of overlooking a leaking pattern.
これに対して、人間の目による判断は、主観的な判断が入るものの、時系列全体を俯瞰して、オートスケーリングをしているため、どんなケースに対しても柔軟に対応できていると考えられる。本発明は、この人間の目による判断機能を、そのまま数値化できないかと考え想到したものである。 On the other hand, although judgment by the human eye involves subjective judgment, it is considered that the entire time series is overlooked and auto-scaling is used, so it can be flexibly dealt with in any case. It is done. The present invention has come up with the idea that the judgment function by the human eye can be converted into a numerical value as it is.
従来の指標は、最新時刻のみの情報で異常判定することが多いが、人間が判断する場合には、最新時刻までに得られた時系列全体を眺めて異常の判断をしている。例えば、図２において最新時刻がt2時刻であればt0時刻からt2時刻までのデータをオートスケーリングすることで判断する。t2時刻ではそれまでのデータ分布と異なるため、多くの人は異常ありと判断することができる。一方、現時点がt1時刻であればt0時刻からt1時刻までのデータをオートスケーリングすることで判断する。t1時刻ではそれまでののデータ分布と大きな差異はないため、人によって差異が生じる可能性がある。すなわち、t2時刻まで概観すると検知できそうだということになるが、t1時刻までであれば、そうとは言い切れない。いずれにしても、ポイントは現時点までに得られたデータ全体をオートスケーリングして判断するということである。 Conventional indicators are often determined to be abnormal based on only the latest time, but when a human makes a determination, an abnormality is determined by looking at the entire time series obtained up to the latest time. For example, if the latest time is t2 in FIG. 2, the determination is made by auto-scaling data from time t0 to time t2. Since t2 time differs from the data distribution so far, many people can judge that there is an abnormality. On the other hand, if the current time is t1 time, the data from t0 time to t1 time is determined by auto scaling. At t1 time, there is no significant difference from the data distribution so far, so there is a possibility that differences may occur depending on the person. In other words, it seems that it can be detected if it is observed until time t2, but it cannot be said that it is until time t1. In any case, the point is that the whole data obtained up to the present time is determined by auto scaling.
そこで、オートスケーリングとして正規化演算を行うとともに、現時点までに得られた時系列データ全体を考慮に入れるためにデータが得られるたびに正規化を逐次更新する処理を行う。こうして得られる逐次正規化値が、例えば、6σ(σ：標準偏差)という値であれば、多くの人が異常だと判断することに対応するし、2σぐらいならば、人によって異常の判断に差異が生ずることに対応するといった具合に、主観的な判断をσの何倍に上限値を設定するかで客観化することができる。 Therefore, normalization is performed as autoscaling, and normalization is sequentially updated every time data is obtained in order to take into account the entire time-series data obtained up to the present time. If the sequential normalized value obtained in this way is, for example, a value of 6σ (σ: standard deviation), this corresponds to the judgment that many people are abnormal. Subjective judgment can be made objective by how many times σ is set as the upper limit value, such as in response to the occurrence of a difference.
まず始めに、得られた時系列データに対して、指数平滑法などによりトレンド成分を抽出する。元の時系列データが比較的安定したものであれば、トレンド成分を抽出せず、元の時系列データを用いてもよい。次に、得られたトレンド成分の値に対して、平均値、分散値を更新する。ただし、過去のトレンド成分の値が保存されていれば、更新式を用いずとも直接計算することができる。 First, trend components are extracted from the obtained time series data by an exponential smoothing method or the like. If the original time series data is relatively stable, the original time series data may be used without extracting the trend component. Next, the average value and the variance value are updated with respect to the obtained trend component values. However, if past trend component values are stored, they can be directly calculated without using an update formula.
次に、求めた平均値と分散値を用いて、逐次正規化の処理を行う。そして、求めた逐次正規化値が、予め設定した上限値を超えたら異常と判断することによって異常検知することができる。上記上限値は、正規化したデータに対するものなので、大量にある監視対象に対して個別に設定する必要はないといった利点がある。 Next, the normalization process is sequentially performed using the obtained average value and variance value. Then, when the obtained sequential normalization value exceeds the preset upper limit value, an abnormality can be detected by determining that it is abnormal. Since the upper limit is for normalized data, there is an advantage that it is not necessary to individually set a large number of monitoring targets.
図１は、本発明に係る機械設備における異常診断システムの構成例を示す図である。図１中、１１はセンサ、１２はデータベース、１３は初期設定部、１４はメモリデータ更新部、１５はトレンド成分抽出部、１６は逐次正規化値算出部、１７は異常判定部、および１８は出力部をそれぞれ表す。 FIG. 1 is a diagram showing a configuration example of an abnormality diagnosis system in mechanical equipment according to the present invention. In FIG. 1, 11 is a sensor, 12 is a database, 13 is an initial setting unit, 14 is a memory data update unit, 15 is a trend component extraction unit, 16 is a sequential normalization value calculation unit, 17 is an abnormality determination unit, and 18 is Each output part is represented.
まず、各種物理量を測定するセンサ１１からの測定データは、データベース１２で記憶される。初期設定部１３では、後段の処理で用いるパラメータの設定、処理を始めるに当たっての初期条件の設定を行う。 First, measurement data from the sensor 11 that measures various physical quantities is stored in the database 12. The initial setting unit 13 sets parameters used in subsequent processing and initial conditions for starting the processing.
次のメモリデータ更新部１４では、最新時刻kにおける各種測定データ(振動データなど)をデータベース３２から取込み、最も古いデータをメモリから破棄する処理を行う。最新時刻kにおける各種測定データ(振動データなど)をセンサ３１からメモリデータ更新部３４へ直接ロードしたり、計算に必要なデータ（過去のデータを含む）を毎回、データベースに直接アクセスする方法でも構わないが、監視点数が多い場合には、処理速度が著しく劣化するので、必要データをメモリにロードしておくことが望ましい。 The next memory data updating unit 14 takes in various measurement data (vibration data and the like) at the latest time k from the database 32 and discards the oldest data from the memory. Various measurement data (vibration data, etc.) at the latest time k may be directly loaded from the sensor 31 to the memory data updating unit 34, or data necessary for calculation (including past data) may be directly accessed each time. However, when the number of monitoring points is large, the processing speed is remarkably deteriorated, so it is desirable to load necessary data into the memory.
トレンド成分抽出部１５では、トレンド成分Sを逐次的に求める。具体的には、機械設備から得られた時刻kにおける信号Y(k)と時刻k-1における信号Y(k-1)から、予め設定した係数αを用いて、以下に示す（１）式により逐次的に算出する。なお、元のデータが比較的安定したものであれば、トレンド成分を抽出せず、元のデータそのものを用いてもよい。 The trend component extraction unit 15 obtains the trend component S sequentially. Specifically, from the signal Y (k) at time k and the signal Y (k-1) at time k-1 obtained from the mechanical equipment, using the coefficient α set in advance, the following equation (1) To calculate sequentially. If the original data is relatively stable, the original data itself may be used without extracting the trend component.
S(k)=S(k-1)+α(S(k-1) - Y(k-1)) ・・・・・（１）
逐次正規化値算出部１６では、得られたトレンド成分の値に対して、平均値、分散値を更新し、更新した平均値、分散値を用いて逐次正規化値を算出する。
S (k) = S (k-1) + α (S (k-1)-Y (k-1)) (1)
The sequential normalized value calculation unit 16 updates the average value and the variance value for the obtained trend component values, and calculates the normalized value using the updated average value and variance value.
先ず、平均値、分散値は、以下に示す（２）、（３）式でそれぞれ求める。 First, the average value and the variance value are obtained by the following expressions (2) and (3), respectively.
但し、Ｓ(k)：時刻kにおけるトレンド成分
μ(k)：時刻kまでのトレンド成分の平均値
σ(k)：時刻kまでのトレンド成分の標準偏差
なお、過去のトレンド成分の値が保存されていれば、上記の更新式を用いずとも直接計算することができる。
Where S (k): Trend component at time k
μ (k): Average trend component up to time k
σ (k): standard deviation of trend component up to time k If the value of the past trend component is stored, it can be directly calculated without using the above update formula.
次に、求めた平均値μ(k)と標準偏差σ(k)を用いて、逐次正規化を以下の（４）式に基づき行い、逐次正規化値ＮＤ(k)を算出する。 Next, using the obtained average value μ (k) and standard deviation σ (k), sequential normalization is performed based on the following equation (4) to calculate a sequential normalized value ND (k).
異常判定部１７では、算出した逐次正規化値ＮＤ(k)が予め設けておいた上限値を超えたら異常と判定し、警報を出力部１８に出力する。逐次正規化値算出部１６で算出した逐次正規化値ＮＤ(k)は監視対象によらず普遍的な情報量であるため、全監視対象に対して同一の上限値を設定することができ、メンテナンスにかける労力を大幅に削減することが可能となる。 The abnormality determination unit 17 determines that an abnormality occurs when the calculated sequential normalization value ND (k) exceeds an upper limit value provided in advance, and outputs an alarm to the output unit 18. Since the sequential normalization value ND (k) calculated by the sequential normalization value calculation unit 16 is a universal information amount regardless of the monitoring target, the same upper limit value can be set for all the monitoring targets. The maintenance labor can be greatly reduced.
図３は、異常発生時の形態が異なる事例に対し本発明を適用した結果の一例を示す図である。図２に示した同じデータ(回転機器の軸受部に設置した振動計で計測された加速度の実効値の時間推移データであり、（ａ）は緩やかに変化するケース、（ｂ）は急激に変化するケース、および（ｃ）はノイズの出現が変化するケース)を用いて、先ず図３（ａ）〜（ｃ）にトレンド成分抽出した結果とともに、対応する逐次正規化値の計算結果を（Ａ）〜（Ｃ）にそれぞれ示している。 FIG. 3 is a diagram illustrating an example of a result of applying the present invention to cases in which the form at the time of occurrence of abnormality is different. The same data shown in FIG. 2 (time transition data of the effective value of acceleration measured with a vibration meter installed in the bearing portion of the rotating device, (a) is a slowly changing case, (b) is abruptly changing And (c) is a case where the appearance of noise changes), together with the results of the trend component extraction shown in FIGS. 3A to 3C, the calculation results of the corresponding sequential normalized values are (A ) To (C).
そして、逐次正規化値の計算結果には、全監視対象に対して同一の固定上限値を適用した場合に異常と判断される異常検出位置も示している。図２に「予知したい時点」として図中に矢印で示したタイミングと比較して、いずれも比較的早期に漏れなく確実に検出できていることが判り、本発明の有効性が確認できる。 Then, the calculation result of the sequential normalization value also indicates an abnormality detection position that is determined to be abnormal when the same fixed upper limit value is applied to all monitoring targets. Compared to the timing indicated by the arrow in FIG. 2 as the “time to be predicted” in FIG. 2, it can be seen that all of them can be reliably detected without omission relatively early, and the effectiveness of the present invention can be confirmed.
１１ センサ
１２ データベース
１３ 初期設定部
１４ メモリデータ更新部
１５ トレンド成分抽出部
１６ 逐次正規化値算出部
１７ 異常判定部
１８ 出力部
DESCRIPTION OF SYMBOLS 11 Sensor 12 Database 13 Initial setting part 14 Memory data update part 15 Trend component extraction part 16 Sequential normalization value calculation part 17 Abnormality determination part 18 Output part
Claims (2)
測定した信号から、平均値および標準偏差を逐次求め、該平均値および標準偏差を用いて逐次正規化値を算出する、逐次正規化値算出部と、
該逐次正規化値算出部で算出された逐次正規化値に基づいて機械設備の異常を検知する異常判定部とを備えることを特徴とする機械設備における異常診断システム。 An abnormality diagnosis system in a mechanical facility that detects an abnormality in the mechanical facility by analyzing a signal measured by a sensor installed in the mechanical facility,
From the measured signal, the average value and the standard deviation are sequentially obtained, and the normalization value is sequentially calculated using the average value and the standard deviation.
An abnormality diagnosis system for a mechanical facility, comprising: an abnormality determination unit that detects an abnormality of the mechanical facility based on the sequential normalization value calculated by the sequential normalization value calculation unit.
前記信号のトレンド成分を抽出するトレンド成分抽出部を備え、
抽出したトレンド成分に基づいて、前記逐次正規化値算出部での処理を行うことを特徴とする機械設備における異常診断システム。 In the abnormality diagnosis system for mechanical equipment according to claim 1,
A trend component extraction unit for extracting a trend component of the signal;
An abnormality diagnosis system for mechanical equipment, wherein the sequential normalization value calculation unit performs processing based on the extracted trend component.
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