JP5426608B2 - Abnormality detection apparatus and abnormality detection method - Google Patents

Description

  The present invention relates to an abnormality detection apparatus and an abnormality detection method for monitoring the state of a plant and detecting the abnormality.

  When the plant is in operation, the operation state of various operation parameters is monitored, and when an abnormality is detected during operation, various measures are taken to return to normal operation. Although such anomaly detection requires identification of the anomaly that has been cultivated over many years, too much relying on the experience of the operator to detect the anomaly will not only increase the human load, but also determine whether it is anomaly. There was also a risk that this was not done uniformly.

  Therefore, a method of automatically detecting an abnormality through a so-called control chart in which various operating parameters during plant operation are periodically plotted and the absolute values and relative changes are observed is employed. Control charts have long been known as a quality control method at production sites. For example, in order to detect production machine abnormalities that may affect product specifications at an early stage, for example, inspection of produced products. The values are plotted every moment. Then, it is determined whether or not the plotted value is included in an allowable range based on a predetermined specification or quality target value, thereby detecting an abnormality in the production machine.

  The simplest method for detecting anomalies through such a control chart is an Xbar chart that plots the average value of product specifications for each lot and manages whether it has risen or fallen out of tolerance. . There is also known a cumulative sum chart that can detect the shift of the average value with high sensitivity and appropriateness. For example, a technique is disclosed in which an abnormality is determined by calculating a cumulative sum of differences between managed data and an exponent-weighted moving average (EWMA) of the managed data (for example, Patent Document 1). Here, the reflection speed to the cumulative sum of the management target data is determined by a fixed weight ratio λ (for example, 0.2).

JP 2010-146197 A

  Since the exponential weighted moving average of Patent Document 1 described above has the characteristics of a first-order low-pass filter (LPF), the average value can be appropriately extracted even when the management target data fluctuates heavily. There is also a drawback that the management target data is reflected with a time constant. Therefore, if a small value is simply adopted as the weighting ratio λ so that an abnormality can be appropriately detected from the management object data with a large fluctuation using the technique of Patent Document 1, the management object data is reflected accordingly. There is a risk that the detection will be delayed and the abnormality detection may also be delayed. When the moving average value is calculated in this way, the moving average value detection accuracy and the responsiveness are in a trade-off relationship, and it is difficult to determine an appropriate weighting ratio λ.

  In view of such a problem, the present invention provides an abnormality detection device and an abnormality detection method capable of appropriately detecting an abnormality while maintaining responsiveness by changing the weighting ratio itself based on management target data. It is intended to provide.

  In order to solve the above-described problem, the abnormality detection apparatus of the present invention uses a moving average value by a monotonically increasing function of an arbitrary order having an absolute value of a difference between managed data and a moving average value of managed data as an argument. A weighting execution unit for weighting the weighted ratio of the management target data in the data, a moving average value deriving unit for deriving a moving average value of the management target data based on the weighted weighted ratio, and the management target data A cumulative sum deriving unit that derives a cumulative sum accumulated based on a difference from the moving average value, and an abnormality determination unit that determines whether there is an abnormality according to the cumulative sum are provided.

…（数式１）
ただし、ｘ（ｉ）は管理対象データ、μ（ｉ−１）は移動平均値の前回値、σ（ｉ−１）は管理対象データと移動平均の差分の標準偏差である。 The weighting execution unit may weight the weighting ratio by the function f (x) of Equation 1.

... (Formula 1)
However, x (i) is the management target data, μ (i−1) is the previous value of the moving average value, and σ (i−1) is the standard deviation of the difference between the management target data and the moving average.

…（数式２）
ただし、μ（ｉ）は移動平均値、λは加重割合である。 The moving average value deriving unit may derive the moving average value based on Equation 2.

... (Formula 2)
However, μ (i) is a moving average value, and λ is a weighted ratio.

…（数式３）

…（数式４）
ただし、ｘ（ｉ）は管理対象データ、μ（ｉ）は移動平均値、σ（ｉ）は管理対象データと移動平均の差分の標準偏差である。かかるσ（ｉ）は、σ（ｉ）＝σ（ｉ−１）または後述する適切な式によって更新される。 The cumulative sum deriving unit may derive the cumulative sum S _H (i) based on Formula 3 or may derive the cumulative sum S _L (i) based on Formula 4.

... (Formula 3)

... (Formula 4)
However, x (i) is the management target data, μ (i) is the moving average value, and σ (i) is the standard deviation of the difference between the management target data and the moving average. Such σ (i) is updated by σ (i) = σ (i−1) or an appropriate formula described later.

  In order to solve the above-mentioned problem, the abnormality detection method of the present invention uses a moving average value by a monotonically increasing function of an arbitrary order having an argument as an absolute value of a difference between managed data and a moving average value of managed data. Weights the weighted ratio of managed data in, derives the moving average of the managed data based on the weighted weighted ratio, and accumulates based on the difference between the managed data and the derived moving average The cumulative sum obtained is derived, and whether or not there is an abnormality is determined according to the cumulative sum.

  According to the present invention, it is possible to appropriately detect an abnormality while maintaining responsiveness by changing the weighting ratio itself based on the management target data.

It is explanatory drawing which showed the time transition of management object data. It is the functional block diagram which showed the electrical structure of the abnormality detection apparatus. It is the flowchart which showed the whole flow of the communication relay method. It is explanatory drawing which showed the time transition of the lubricating oil temperature of the gas turbine by an exponential weighted moving average. It is explanatory drawing which showed the time transition of the lubricating oil temperature of the gas turbine by the weight variable type moving average by this embodiment.

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The dimensions, materials, and other specific numerical values shown in the embodiments are merely examples for facilitating the understanding of the invention, and do not limit the present invention unless otherwise specified. In the present specification and drawings, elements having substantially the same function and configuration are denoted by the same reference numerals, and redundant description is omitted, and elements not directly related to the present invention are not illustrated. To do.

  In this embodiment, the state of a plant is monitored through a control chart, and the abnormality is detected. Various objects such as a gas turbine, a gas engine, and the like can be applied as targets for such abnormality detection. In the present embodiment, a weight variable moving average of the management target data plotted in the control chart is derived, and an abnormality is detected by the cumulative sum of the fluctuation values of the management target data based on the weight variable moving average. Here, first, the process of deriving the cumulative sum that is the premise of the present embodiment will be described, and then the detailed process of the present embodiment will be described.

  FIG. 1 is an explanatory diagram showing a time transition of management target data. As shown in FIG. 1A, the management target data itself swings up and down each time it is acquired. In a plant or the like that is a management target of the present embodiment, the target to be managed is not a fixed value, but varies in the long run (moving average value). In the present embodiment, fluctuations in the management target data over a long period are allowed, and only short-term fluctuations are recognized as abnormal. Whether the management target data fluctuates in the long term or in the short term is determined based on whether or not the data is in accordance with the moving average value. First, the moving average value of the management target data will be described in detail.

…（数式５）
ここで、μ（ｉ−１）はμ（ｉ）の前回値であり、λ（０＜λ＜１）は加重割合を示す。移動平均値μ（ｉ）の計算において、計算結果である移動平均値μ（ｉ）の精度と即応性とはトレードオフの関係にあり、管理対象に応じて適切な加重割合λを定める。例えば、加重割合λを０．０１４として、当該移動平均値μ（ｉ）を計算すると、図１（ａ）の管理対象データから、図１（ｂ）のような移動平均値μ（ｉ）が求まる。 For example, when an exponentially weighted moving average (EWMA) that has been known for a long time is used, the moving average value μ (i) when the management target data sequentially acquired periodically is x (i) is expressed by the following formula: As shown in FIG. However, i is an integer for representing the timing at which the management target data is acquired in time series. Unless otherwise specified, i is the latest acquisition timing, and n is the previous acquisition timing (n is also an integer). It shows with.

... (Formula 5)
Here, μ (i−1) is the previous value of μ (i), and λ (0 <λ <1) indicates a weighted ratio. In the calculation of the moving average value μ (i), the accuracy and responsiveness of the moving average value μ (i) as a calculation result are in a trade-off relationship, and an appropriate weighting ratio λ is determined according to the management target. For example, when the weighted ratio λ is 0.014 and the moving average value μ (i) is calculated, the moving average value μ (i) as shown in FIG. 1B is obtained from the management target data in FIG. I want.

…（数式３）

…（数式４）
ここで、移動平均値μ（ｉ）はターゲット値に相当し、σ（ｉ）は管理対象データｘ（ｉ）と移動平均値μ（ｉ）の差分の標準偏差であり、σ（ｉ）＝σ（ｉ−１）または後述する適切な式によって更新される。また、ｍａｘ（）は、括弧内の複数の値から最大値を抽出する関数であり、ｍｉｎ（）は、括弧内の複数の値から最小値を抽出する関数である。したがって、累積和Ｓ_Ｈ（ｉ）の最小値は０となり、累積和Ｓ_Ｌ（ｉ）の最大値は０となる。 Then, the moving average value μ (i) is subtracted from the management target data x (i), and a predetermined threshold k (k is a constant) is subtracted from the value divided by the standard deviation σ (i), and this is used as a candidate value for abnormality. Accumulate. Then, for example, when the cumulative sum exceeds a predetermined value, it is recognized as abnormal. The cumulative sum detects both the cumulative sum S _H (i) when shifted upward from the moving average value corresponding to the target and the cumulative sum S _L (i) when shifted downward, and its derivation formula is as follows: Equations 3 and 4 are obtained.

... (Formula 3)

... (Formula 4)
Here, the moving average value μ (i) corresponds to the target value, σ (i) is a standard deviation of the difference between the management target data x (i) and the moving average value μ (i), and σ (i) = It is updated by σ (i−1) or an appropriate formula described later. Further, max () is a function that extracts a maximum value from a plurality of values in parentheses, and min () is a function that extracts a minimum value from a plurality of values in parentheses. Therefore, the minimum value of the cumulative sum S _H (i) is 0, and the maximum value of the cumulative sum S _L (i) is 0.

Equations 3 and 4 are added to S _H (i) and from S _L (i) when the measured value is more than kσ away from the target value (= moving average value μ (i)) regarded as a normal value. Means to subtract. However, when the deviation of the measured value is less than _k?, Subtracting from _S H _(i), the addition is performed in the _{S L (i), S H} (i), a 0 none of the _S L (i) It doesn't change beyond. At this time, the value of k is normally “0.5”. Therefore, μ + kσ and μ−kσ are expressed as shown in FIG. 1C. The accumulated sum S _H is accumulated so that addition is performed when the measured value deviates upward from the width, and subtraction is performed when the measured value does not deviate. (I) is as shown in FIG.

By using such a cumulative sum S _H (i) as a control chart, it is possible to determine short-term fluctuations regarding the management target data x (i). For example, an abnormality can be detected when the value of the cumulative sum S _H (i) in FIG.

  However, as described above, since the exponential weighted moving average according to Equation 5 has the characteristics of a first-order low-pass filter (LPF), the management target data x (i) is reflected in the moving average value μ (i). It will be delayed with a long time constant. Therefore, when the management target data x (i) changes greatly stepwise due to regular maintenance or the like, the amount of change is reflected in the moving average value μ (i) very slowly. Then, since the moving average value μ (i) is delayed from becoming the original target value, an abnormality during that time cannot be properly detected.

  Therefore, when the management target data x (i) and the previous moving average value μ (i−1) are significantly shifted, the convergence speed of the moving average value μ (i) is increased (time constant is shortened). Can be considered. In this embodiment, according to the absolute value of the difference between the management object data x (i) and the previous moving average value μ (i−1), the management object data x (i) and the previous The convergence speed of the moving average value μ (i) is changed by changing the weighting with the moving average value μ (i−1). For example, when the absolute value of the difference between the management target data x (i) and the previous moving average value μ (i−1) is small, the moving average value μ (i−1) is weighted as a steady state, When the management target data x (i) and the previous moving average value μ (i−1) are significantly different, the management target data x (i) is weighted heavily.

…（数式１） Here, for example, continuously in proportion to the absolute value | x (i) −μ (i−1) | of the difference between the management target data x (i) and the previous moving average value μ (i−1). The following monotonically increasing function is used as the weighting ratio λ (multiplying the weighting ratio λ).

... (Formula 1)

…（数式２） By applying Equation 1 to Equation 5, Equation 2 can be derived. The calculation according to Equation 2 is called a weight variable moving average.

... (Formula 2)

  In the present embodiment, the moving average value μ according to the absolute value | x (i) −μ (i−1) | of the difference between the management target data x (i) and the previous moving average value μ (i−1). (I) Change its own convergence speed. Thus, even when the management target data x (i) changes greatly in stages, the moving average value μ (i) quickly follows the management target data x (i) according to the amount of change. Become. Hereinafter, a specific configuration for realizing such abnormality detection will be described.

(Abnormality detection device 100)
FIG. 2 is a functional block diagram showing an electrical configuration of the abnormality detection apparatus 100. As shown in FIG. The abnormality detection apparatus 100 includes a data acquisition unit 110, a data holding unit 112, an operation unit 114, a display unit 116, and a control unit 118.

  The data acquisition unit 110 acquires management target data x (i) that is data related to an arbitrary parameter of a plant that is a management target. Here, it is assumed that temperature information of the lubricating oil of the gas turbine or the gas engine is acquired. The data holding unit 112 is configured by a storage medium such as an HDD, a flash memory, or a RAM, and holds programs and various data used by the control unit 118.

  The operation unit 114 includes a keyboard, a pointing device, a cross key, a joystick, a touch panel, and the like, and receives an operation input from an operator. The display unit 116 is configured by a liquid crystal display, an organic EL (Electro Luminescence) display, and the like, and displays a control chart or the like, or performs a notification display when an abnormality is detected.

  The control unit 118 is constituted by a semiconductor integrated circuit including a central processing unit (CPU), a ROM storing a program, a RAM as a work area, and the like, and operates abnormally in cooperation with the data holding unit 112 and other electronic circuits. The entire detection device 100 is managed and controlled. The control unit 118 also functions as a weighting execution unit 130, a moving average value deriving unit 132, a cumulative sum deriving unit 134, and an abnormality determining unit 136.

  The weighting execution unit 130 acquires the management target data x (i) acquired by the data acquisition unit 110 and the moving average value μ (i−) derived by the moving average value deriving unit 132 and held in the data holding unit 112 during the previous calculation. When the absolute value | x (i) −μ (i−1) | of the difference from 1) increases, the weighting ratio λ is weighted by a function f (x) of an arbitrary order that monotonously increases.

…（数式１） Here, the function f (x) gradually decreases as the function that increases gradually as | x (i) −μ (i−1) | increases (ie, | x (i) −μ (i−1) |) decreases. The order of the function does not matter as long as it simply increases. The order includes not only the case where the index is 1 or more but also the case where the index is less than 1. In the present embodiment, it is assumed that the weighting execution unit 130 weights the weighting ratio λ with a linear function f (x) of x (i).

... (Formula 1)

…（数式６）
ここで、ａ_ｋ（ｋ＝０，１，２…ｎ）は０以上の係数である。 Of course, weighting may be performed by an n-order function f (x) of | x (i) −μ (i−1) |

... (Formula 6)
Here, a _k (k = 0, 1, 2,... N) is a coefficient of 0 or more.

…（数式７）
しかし、実際に標準偏差σ（ｉ−１）が変化する理由は考えにくく、標準偏差σ（ｉ−１）を更新することの効果に乏しいので、本実施形態においては、λ_Ｓ＝１とし、σ（ｉ−１）は固定値とする。したがって、σ（ｉ）もσ（ｉ）＝σ（ｉ−１）となり、固定値で表される。 Σ (i−1) in Equation 1 is the standard deviation of the difference between the management target data and the moving average value, and can be derived using Equation 7.

... (Formula 7)
However, the reason why the standard deviation σ (i−1) actually changes is unlikely, and since the effect of updating the standard deviation σ (i−1) is poor, λ _S = 1 in this embodiment, σ (i−1) is a fixed value. Therefore, σ (i) is also σ (i) = σ (i−1), and is represented by a fixed value.

…（数式２） The moving average value deriving unit 132 derives the moving average value μ (i) of the management target data x (i) based on the weighting ratio λ weighted by the weighting execution unit 130 according to Equation 2. The derived moving average value μ (i) is used in the cumulative sum deriving unit 134 and also held in the data holding unit 112 for the next weighting by the weighting execution unit 130.

... (Formula 2)

The cumulative sum deriving unit 134 accumulates the cumulative sum S _H (i) and the cumulative sum based on the difference between the management target data x (i) and the moving average value μ (i) derived by the moving average value deriving unit 132. Derive S _L (i).

For example, in the present embodiment, the cumulative sum deriving unit 134 has a difference between the management target data x (i) and the derived moving average value μ (i) exceeding a predetermined value kσ (k is 0.5, for example). The cumulative sums S _H (i) and S _L (i) are accumulated so that the sign of the change changes depending on whether or not.

In this way, the fluctuation within the distance kσ of the management target data x (i) from μ (i) is accumulated unintentionally by performing cumulative calculation in a direction in which the absolute value of the cumulative sum decreases. It is possible to avoid an event in which the magnitudes of the sums S _H (i) and S _L (i) increase. In addition, short-term abnormalities to be truly extracted can be reliably accumulated as a value exceeding kσ, and the larger the value, the more the cumulative sums S _H (i) and S _L (i) are affected. Get higher. Therefore, it is possible to appropriately detect an abnormality through the cumulative sums S _H (i) and S _L (i).

…（数式３）

…（数式４） Specifically, the cumulative sum deriving unit 134 derives the cumulative sum S _H (i) based on Equation 3 and the cumulative sum S _L (i) based on Equation 4. The derived cumulative sums S _H (i) and S _L (i) are held in the data holding unit 112 to be used for the next calculation.

... (Formula 3)

... (Formula 4)

The abnormality determination unit 136 determines whether or not the current plant state is abnormal based on the cumulative sums S _H (i) and S _L (i) derived by the cumulative sum deriving unit 134 and displays the result. To display. The overall operation flow of the abnormality detection apparatus 100 will be described below.

(Abnormality detection method)
FIG. 3 is a flowchart showing the overall flow of the communication relay method. In the abnormality detection method, processing is started by a periodic timer interruption at a predetermined time interval. When a timer interruption occurs, first, the data acquisition unit 110 of the abnormality detection apparatus 100 acquires the management target data x (i) (S200).

  Then, the weighting execution unit 130 reads the previous moving average value μ (i−1) held in the data holding unit 112 (S202), reads the management target data x (i) acquired by the data acquisition unit 110, and the reading. When the absolute value | x (i) −μ (i−1) | of the difference from the moving average value μ (i−1) increases, the weighting ratio λ is increased by an arbitrary order function f (x) that increases monotonously. Is weighted (S204). Subsequently, the moving average value deriving unit 132 derives the moving average value μ (i) according to Equation 2 (S206), and holds the derived moving average value μ (i) in the data holding unit 112 (S208). .

Next, the cumulative sum deriving unit 134 reads the previous cumulative sums S _H (i−1) and S _L (i−1) held in the data holding unit 112 (S210), and is based on Equations 3 and 4. The cumulative sum S _H (i) and the cumulative sum S _L (i) are derived (S212), and the derived cumulative sums S _H (i) and S _L (i) are held in the data holding unit 112 (S214). ). The abnormality determination unit 136 determines whether there is an abnormality according to the cumulative sums S _H (i) and S _L (i) derived by the cumulative sum deriving unit 134, and displays the result on the display unit 116 (S216). In this way, the operator can quickly and easily grasp the plant abnormality.

  Even when the management target data x (i) changes greatly in steps by changing the weighting ratio λ itself based on the management target data x (i) by the abnormality detection apparatus 100 described above, the moving average value The time constant of μ (i) can be lowered to quickly follow the management target data x (i). Therefore, it is possible to appropriately detect an abnormality while maintaining responsiveness.

(Examination of effect)
Hereinafter, in order to show the effect of the present embodiment, the abnormality detection by the conventional exponential weighted moving average (EWMA) and the abnormality detection by the weight variable moving average of the present embodiment are compared.

FIG. 4 is an explanatory diagram showing the time transition of the lubricating oil temperature of the gas turbine according to a conventional exponentially weighted moving average. Here, the moving average value μ (i) is obtained based on Equation 5. Accordingly, the moving average value μ (i) changes as shown by the dotted line in FIG. 4A, and the range of ± kσ with respect to the moving average value μ (i) has an upper limit of one-dot chain line and a lower limit of two-dot chain line. It changes as follows. At this time, the magnitudes of the cumulative sums S _H (i) and S _L (i) increase when the measured value exceeds ± kσ, and decrease when the measured value does not exceed ± kσ. As a result, the cumulative sum S _H (i) is as shown in FIG. 4B, and the cumulative sum S _L (i) is as shown in FIG. 4C.

  The transition of the lubricating oil temperature shown in FIG. 4 is normal and should not be determined to be abnormal immediately with the transition. However, when the lubricating oil temperature rises in a relatively short period of time, such as period A or period C in FIG. 4 (a), for example, the operator grasps this and immediately when the rise continues. It is necessary to prepare so that it can respond to.

When the exponential weighted moving average is used, it can be understood that the short-term temperature rise in the period A in FIG. 4A also appears in the cumulative sum S _H (i) shown in FIG. . However, when periodic maintenance is performed at time B and the lubricating oil temperature falls stepwise, the rising phenomenon is shown in FIG. It is difficult to grasp with the cumulative sum S _H (i) shown in b).

This is because the moving average value μ (i) is calculated based on Formula 5 with the weighting ratio λ fixed, so that when the management target data x (i) changes suddenly as at time B, the moving average value This is because it takes time until μ (i) sufficiently follows the management target data x (i) (until it converges). Then, after the management target data x (i) suddenly changes at time B, the moving average value μ (i) gradually approaches the management target data x (i), and finally the management target data x (i) in the middle of the period C. It is calm around. Thus, if it takes time for the moving average value μ (i) to converge to a value that sufficiently represents the management target data x (i) at that time, the temperature tends to rise uniformly in the period C. Even if it exists, the cumulative sum S _H (i) in FIG. 4B does not rise immediately, but rises only in the second half of the period C. This delay is eliminated by the weight variable moving average of this embodiment.

FIG. 5 is an explanatory view showing the time transition of the lubricating oil temperature of the gas turbine by the weight variable moving average according to the present embodiment. Here, the moving average value μ (i) is obtained based on Equation 2. Therefore, the moving average value μ (i) changes as shown by the dotted line in FIG. 5A, and the range of ± kσ with respect to the moving average value μ (i) is the upper limit with a one-dot chain line and the lower limit with a two-dot chain line. It changes as follows. At this time, the magnitudes of the cumulative sums S _H (i) and S _L (i) increase when the measured value exceeds ± kσ, and decrease when the measured value does not exceed ± kσ. As a result, the cumulative sum S _H (i) is as shown in FIG. 5B, and the cumulative sum S _L (i) is as shown in FIG. 5C.

In the period A of FIG. 5A, as in FIG. 4B, it can be understood that the short-term temperature rise appears in the cumulative sum S _H (i) shown in FIG. 5B and can be easily grasped. Further, when the exponential weighted moving average is used, it is difficult to grasp the increase from the cumulative sum S _H (i) as shown in FIG. 4B. However, when the weight variable moving average is used, , After the management target data x (i) suddenly changes at the time point B, the moving average value μ (i) quickly follows the management target data x (i), and the cumulative sum S _H ( It can be seen that i) rises in the first half of period C.

Here, assuming that the abnormality determination value of the cumulative sum S _H (i) is “50 or more”, with regard to the short-term increase in the period A, the weight variable according to the present embodiment is also applied to the conventional exponential weighted moving average value. Even in the mold moving average value, an abnormality can be detected simultaneously at 7/1. However, regarding the short-term rise in the period C after passing through the time point B, the abnormal value can be detected, but the conventional exponential weighted moving average is 8/9 (see FIG. 4B). On the other hand, in the weight variable moving average of this embodiment, the abnormality can be found in 8/5. As described above, by changing the weighting ratio itself based on the management target data, it is possible to appropriately detect the abnormality while maintaining the responsiveness.

Further, the cumulative sum _S L (i) is 4 (c) and as can be seen by comparing the FIG. 5 (c), when the tentatively "-50 hereinafter" the abnormality determination value of the cumulative sum _S L (i) It can be understood that both the conventional exponential weighted moving average and the weight variable moving average of the present embodiment detect the abnormality almost simultaneously (7/25). Although the weight variable moving average of the present embodiment is advantageous for short-term increases, the moving average value μ (i) according to the present embodiment is weighted by the cumulative sum S _H (i) and the cumulative sum. Needless to say, the weight-variable moving average of the present embodiment is advantageous even for a short-term decline because it affects the sum S _L (i) equally.

  As mentioned above, although preferred embodiment of this invention was described referring an accompanying drawing, it cannot be overemphasized that this invention is not limited to this embodiment. It will be apparent to those skilled in the art that various changes and modifications can be made within the scope of the claims, and these are naturally within the technical scope of the present invention. Understood.

  Note that each step in the abnormality detection method of the present specification does not necessarily have to be processed in time series in the order described in the flowchart, and may include processing in parallel or by a subroutine.

  INDUSTRIAL APPLICABILITY The present invention can be used for an abnormality detection device and an abnormality detection method for monitoring the state of a plant and detecting the abnormality.

DESCRIPTION OF SYMBOLS 100 ... Abnormality detection apparatus 110 ... Data acquisition part 112 ... Data holding part 130 ... Weighting execution part 132 ... Moving average value derivation part 134 ... Cumulative sum derivation part 136 ... Abnormality determination part

Claims (5)

A weighting execution unit that weights the weighted ratio of the management target data in the moving average value by a monotonically increasing function of an arbitrary order using the absolute value of the difference between the management target data and the moving average value of the management target data as an argument; ,
A moving average value deriving unit for deriving a moving average value of the management target data based on the weighted ratio in which the weighting is performed;
A cumulative sum deriving unit for deriving a cumulative sum accumulated based on a difference between the management target data and the derived moving average value;
An abnormality determination unit that determines whether there is an abnormality according to the cumulative sum;
An abnormality detection device comprising: The anomaly detection apparatus according to claim 1, wherein the weighting execution unit weights the weighting ratio using a function f (x) of Formula 1.

... (Formula 1)
However, x (i) is the management target data, μ (i−1) is the previous value of the moving average value, and σ (i−1) is the standard deviation of the difference between the management target data and the moving average. The abnormality detection device according to claim 2, wherein the moving average value deriving unit derives the moving average value based on Formula 2.

... (Formula 2)
However, μ (i) is a moving average value, and λ is a weighted ratio. The cumulative sum deriving unit derives a cumulative sum S _H (i) based on Formula 3 or derives a cumulative sum S _L (i) based on Formula 4. Anomaly detection device.

... (Formula 3)

... (Formula 4)
However, x (i) is the management target data, μ (i) is the moving average value, and σ (i) is the standard deviation of the difference between the management target data and the moving average. The weighting ratio of the management target data in the moving average value is weighted by a monotonically increasing function of an arbitrary order using the absolute value of the difference between the management target data and the moving average value of the management target data as an argument,
Deriving the moving average value of the management target data based on the weighted ratio that has been weighted,
Deriving the cumulative sum accumulated based on the difference between the managed data and the derived moving average value,
An abnormality detection method characterized by determining whether or not there is an abnormality according to the cumulative sum.

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