WO2012073289A1 - Dispositif de diagnostic d'installation et procédé de diagnostic d'installation - Google Patents

Dispositif de diagnostic d'installation et procédé de diagnostic d'installation Download PDF

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Publication number
WO2012073289A1
WO2012073289A1 PCT/JP2010/007025 JP2010007025W WO2012073289A1 WO 2012073289 A1 WO2012073289 A1 WO 2012073289A1 JP 2010007025 W JP2010007025 W JP 2010007025W WO 2012073289 A1 WO2012073289 A1 WO 2012073289A1
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Prior art keywords
plant
state
model
measurement signal
normal
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PCT/JP2010/007025
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English (en)
Japanese (ja)
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関合孝朗
江口徹
楠見尚弘
深井雅之
村上正博
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株式会社日立製作所
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Priority to JP2012546576A priority Critical patent/JP5484591B2/ja
Priority to PCT/JP2010/007025 priority patent/WO2012073289A1/fr
Publication of WO2012073289A1 publication Critical patent/WO2012073289A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Definitions

  • the present invention relates to a plant diagnosis apparatus and a plant diagnosis method.
  • the plant diagnostic device detects abnormalities or accidents based on measurement signals from the plant when abnormal transients or accidents occur in the plant.
  • ART is a technique for classifying multidimensional data into categories according to their similarity.
  • a learning signal is obtained from a past measurement signal in which operation data of the plant is recorded. Extract as Then, using ART, the learning data is classified into a plurality of categories (normal categories) to create a normal model. Next, the current measurement signal of the plant is classified into categories by ART. When the current measurement signal does not match the normal model, that is, when it cannot be classified into the normal category, a new category (new category) is generated. In other words, the occurrence of a new category means that the trend of the measurement signal has changed and the state of the plant has changed. Therefore, it is a technique for determining the occurrence of an abnormality based on the occurrence of a new category and diagnosing an abnormality when the occurrence rate of the new category exceeds a threshold value.
  • the plant diagnosis device is required to detect a change in the state of the plant before the occurrence of the abnormality in order to secure time for considering countermeasures for the abnormality. In order to realize this, it is necessary to detect a predictive state on the way from the normal state to the abnormal state.
  • a plant control device has a function of generating an alarm when a measurement signal deviates from a predetermined range. If an alarm is generated in accordance with an abnormality, learning data excluding data in an abnormal state can be created by excluding data in a period during which the alarm is generated from past measurement signals.
  • this learning data includes data of a predictive state on the way from a normal state to an abnormal state where an alarm is generated. Therefore, when a normal model is constructed using this learning data, the predictive state is diagnosed as normal, and the predictive state cannot be detected.
  • An object of the present invention is to provide a plant diagnostic apparatus and a plant diagnostic method that can detect a sign state in the middle of a plant state from a normal state to an abnormal state.
  • the plant diagnosis apparatus for diagnosing the operation state of the plant based on the measurement signal obtained by measuring the state quantity from the plant of the present invention and displaying the diagnosis result on the image display apparatus measured the state quantity of the plant in the plant diagnosis apparatus.
  • a classification unit that classifies the same category, a period determination unit that evaluates a difference in the tendency of the measurement signal based on the classification result of the category in the classification unit, and determines a normal period, a precursor period, and an abnormal period, and the normal period
  • a model construction unit that constructs a normal model using the measurement signal of, and the diagnostic means classifies the current time measurement signal into a normal model constructed by the model construction unit If the measurement signal at the current time is included in the normal model, the plant is diagnosed as normal and displayed on the image display device, and the measurement signal at the current time is included
  • the plant diagnosis device that diagnoses the operation state of the plant based on the measurement signal input from the plant of the present invention and displays the diagnosis result on the image display device measures the state quantity of the plant in the plant diagnosis device.
  • the learning means for constructing a model used for diagnosis using the measurement signal, and the diagnostic means for diagnosing the operation state of the plant using the model constructed by the learning means, the learning means having data having similar values A period determining unit that evaluates a difference in the tendency of the measurement signal based on a classification result of the category in the classification unit, and determines a normal period, a predictive period, and an abnormal period, and the normal
  • a normal model is constructed using the measurement signal of the period
  • a sign model is constructed using the measurement signal of the sign period
  • an abnormality model is constructed using the measurement signal of the abnormal period.
  • the diagnosis means determines whether the current time measurement signal is classified into a normal model, a predictive model, or an abnormal model, and the current time measurement signal is a normal model or a predictive model. If it is included in the abnormal model, the plant is diagnosed as being in the normal, predictive or abnormal state corresponding to the model and displayed on the image display device, and the measurement signal at the current time is the normal model, predictive model, When it is not included in any of the abnormal models, the plant is diagnosed as an unknown state that has not been experienced in the past, and is displayed on the image display device.
  • the plant diagnosis method for diagnosing the operation state of the plant using the measurement signal obtained by measuring the state quantity of the plant of the present invention and displaying the diagnosis result is a measurement signal obtained by measuring the state quantity of the plant as a plant diagnosis device.
  • Learning means for constructing a model to be used for diagnosis, and diagnostic means for diagnosing the operating state of the plant using the model constructed by the learning means, and learning means for measuring the measurement signal of the state quantity of the plant The learning means classifies data having similar values among the measurement signals into the same category, and evaluates the difference in the tendency of the measurement signals based on the classification results of the categories, thereby normal periods and predictive periods.
  • Each abnormal period is determined, a normal model is constructed using the measurement signal of this normal period, and the measurement signal obtained by measuring the state quantity of the plant is input to the diagnostic means.
  • the diagnostic means determines whether or not the current time measurement signal is classified as a normal model, and if the current time measurement signal is included in the normal model, the image is obtained by diagnosing that the plant is in a normal state. When it is displayed on the display device and the measurement signal at the current time is not included in the normal model, the plant is diagnosed as an unknown state that has not been experienced in the past, and is displayed on the image display device.
  • the plant diagnosis method for diagnosing the operation state of the plant based on the measurement signal input from the plant of the present invention and displaying the diagnosis result uses a measurement signal obtained by measuring the state quantity of the plant as a plant diagnosis device.
  • Learning means for constructing a model to be used for diagnosis, and diagnostic means for diagnosing the operation state of the plant using the model constructed by the learning means, and the measurement signal obtained by measuring the state quantity of the plant is used as the learning means.
  • Input classify data having similar values among the measurement signals in the learning means in the same category, evaluate the difference in the tendency of the measurement signals based on the classification results of this category, normal period, predictive period, Determine each abnormal period, construct a normal model using the measurement signal of the normal period, construct a predictor model using the measurement signal of the sign period, An abnormal model is constructed using the measurement signal of the abnormal period, and the measurement signal obtained by measuring the state quantity of the plant is input to the diagnosis unit, and the measurement signal at the current time is the normal model, predictor model, abnormal model If it is determined which model is classified, and the measurement signal at the current time is included in one of the normal model, predictive model, or abnormal model, the plant corresponds to normal, predictive, or abnormal corresponding to the model If the measurement signal at the current time is not included in any of the normal model, predictive model, and abnormal model, the plant is in an unknown state that has not been experienced in the past. It is characterized by being diagnosed and displayed on an image display device.
  • the plant diagnostic apparatus and the plant diagnostic method which can detect the precursor state in the middle of the state of a plant from a normal state to an abnormal state are realizable.
  • a plant diagnostic apparatus and a plant diagnostic method capable of detecting a change in the state of the plant at an early stage before the plant reaches an abnormal state are obtained.
  • FIG. 2 The control block diagram which shows the structure of the diagnostic apparatus of the plant which is one Example of this invention.
  • FIG. 2 is an explanatory diagram illustrating a situation in which diagnosis is performed by operating both a learning mode and a diagnostic mode for each measurement signal sampling period in the basic operation of the plant diagnosis apparatus illustrated in FIG. 1.
  • FIG. 1 An explanatory diagram showing a situation in which a normal state learning mode is operated every predetermined set period and only a diagnosis mode is operated every sampling period for diagnosis.
  • FIG. 2 is an explanatory diagram showing a situation in which diagnosis is performed by inputting an external input signal and operating a learning mode and a diagnostic mode at an arbitrary timing in the basic operation of the plant diagnostic apparatus shown in FIG. 1.
  • Explanatory drawing which shows the example of mounting of the data classification function in the classification
  • the block diagram which shows the structure of the F0 layer which comprises the ART module with which the classification
  • Explanatory drawing which shows an example of the classification result which classified the measurement signal acquired from the plant shown in Drawing 4 (a) into a category. It is a figure explaining the 1st implementation example of the period determination part which builds the learning means in the diagnostic apparatus of the plant shown in FIG. 1, Comprising: The boundary time of the normal state of a plant operating state, and a precursor state, and a precursor state Explanatory drawing which shows the condition of the boundary time of an abnormal state.
  • Explanatory drawing which shows the algorithm which determines the boundary time of the normal state and predictive state about the operation state of the plant shown to Fig.5 (a), and the boundary time of predictive state and abnormal state. It is explanatory drawing which shows the 2nd implementation example of the period determination part which builds the learning means in the diagnostic apparatus of the plant shown in FIG. 1, Comprising: Explanatory drawing which showed the condition which divides
  • Explanatory drawing which shows the aspect of the data preserve
  • Explanatory drawing which shows the relationship between the category number of the data preserve
  • Explanatory drawing which shows the relationship between the time of the data preserve
  • Explanatory drawing which shows the screen which displayed the diagnostic result of the plant diagnosed with the diagnostic apparatus of the plant shown in FIG. The explanatory view which displayed the boundary time of the normal state and the predictor state, and the boundary time of the predictor state and the abnormal state determined by the period determining unit constituting the learning means provided in the plant diagnosis apparatus shown in FIG. 1 as default values.
  • FIG. 1 is a block diagram illustrating a plant diagnosis apparatus according to an embodiment of the present invention.
  • the diagnosis apparatus 200 diagnoses the state of the plant 100.
  • the diagnostic device 200 includes a learning unit 400 and a diagnostic unit 800 as arithmetic units constituting the diagnostic device 200.
  • the diagnostic apparatus 200 includes a measurement signal database 310 and a diagnostic model database 320 as databases.
  • the database is abbreviated as DB.
  • the database of the measurement signal database 310 and the diagnostic model database 320 records computerized information and is usually called an electronic file (electronic data).
  • the learning unit 400 creates a normal model, a predictive model, and an abnormal model from measurement signals obtained by measuring the operation state of the plant 100, based on accumulated data obtained by accumulating measurement signals of past operation states of the plant 100. It has a function.
  • the diagnosis unit 800 compares the normal model, the predictive model, and the abnormal model created by the learning unit 400 with the measured signal data of the plant 100, and to which model the measured signal data belongs. It has a function to determine whether it is present.
  • the learning unit 400 includes a classification unit 500, a period determination unit 600, and a model construction unit 700.
  • the classification unit 500 has a function of classifying the accumulated data, which is the measurement signal 4 of the past operation state of the plant 100 accumulated in the measurement signal database 310, into similar groups.
  • the period determining unit 600 has a function of determining a period during which the operation state of the plant 100 is in a normal state, a predictor state, or an abnormal state based on the classification result 5 and the measurement signal 4 by the classification unit 500. It is what has.
  • the model construction unit 700 has a function of constructing a normal model, a predictive model, or an abnormal model based on the classification result 5 of the classification unit 500, the basic information 6 of the period determination unit 600, and the measurement signal 4. It is.
  • the diagnostic apparatus 200 includes an external input interface 210 and an external output interface 220 as interfaces with the outside.
  • the measurement signal 1 which measured the various state quantities which are the operation states of the plant 100 via the external input interface 210, and the external input device 910 comprised of the keyboard 920 and the mouse 930 provided in the operation management room 900.
  • the external input signal 2 created by the operation is taken into the diagnostic device 200.
  • the image display information 10 is output to the image display device 940 provided in the operation management room 900 via the external output interface 220.
  • the learning unit 400, the diagnosis unit 800, the measurement signal database 310, and the diagnosis model database 320 are provided in the diagnosis apparatus 200. It may be arranged outside of 200 and only data may be communicated between these devices.
  • the measurement signal 1 obtained by measuring various state quantities of the plant 100 is taken in via the external input interface 210, and the measurement signal of the plant 100 is received from the external input interface 210. 3 is stored in the measurement signal database 310 installed in the diagnostic apparatus 200.
  • the learning unit 400 installed in the diagnostic apparatus 200 includes a classification unit 500, a period determination unit 600, and a model construction unit 700.
  • the learning unit 400 divides past accumulated data, which is the measurement signal 1 obtained by measuring various state quantities of the plant 100 stored in the measurement signal database 310, into normal data, predictive data, and abnormal data, and uses each data. It has the function of constructing normal models, predictive models, and abnormal models.
  • the normal model, predictive model, and abnormal model are created by operating the classification unit 500, the period determination unit 600, and the model construction unit 700 installed in the learning unit 400.
  • the classification unit 500 installed in the learning unit 400 classifies the measurement signal 4 that is the past accumulated data stored in the measurement signal database 310 into a group of similar data, and learns as a classification result 5.
  • the result is output to the period determining unit 600 of the means 400. A method of mounting the classification unit 500 will be described later with reference to FIG.
  • the period determination unit 600 installed in the learning unit 400 uses the measurement signal 4 which is past accumulated data stored in the measurement signal database 310 and the input information of the classification result 5 by the classification unit 500 to measure the measurement signal. Evaluate the difference in the four trends. Then, based on the evaluation result, the data period of the measurement signal 4 is divided into a normal period, a sign period, and an abnormal period. This result is output as the period information 6 to the model construction unit 700 of the learning unit 400. A method for mounting the period determination unit 600 will be described later with reference to FIGS. 5 and 6.
  • the model construction unit 700 installed in the learning unit 400 creates a normal model, a predictor model, and an abnormal model for each of the normal period, the predictor period, and the abnormal period. Then, each of these created models is output to the diagnostic model database 320 as model information 7.
  • the diagnostic means 800 installed in the diagnostic apparatus 200 diagnoses the operating state of the plant 100 by referring to the normal model, predictive model, and abnormal model of the diagnostic model database 320 with respect to the input of the measurement signal 4.
  • the diagnosis result 9 is output.
  • the diagnostic unit 800 determines which model of the normal model, the predictive model, and the abnormal model of the diagnostic model database 320 the measurement signal 4 of the plant 100 belongs to. As a result, it is diagnosed whether the current operation state of the plant 100 is normal, predictive, or abnormal, and the diagnosis result 9 is output to the diagnostic display device 940 and displayed.
  • the diagnosis result 9 Is output to the diagnostic display device 940 and displayed.
  • diagnosis means 800 as a simple means, it is possible to diagnose a normal or other unknown state using only a normal model. Details of the diagnostic means 800 will be described later with reference to FIG.
  • the diagnosis result 9 for the current operation state of the plant 100 diagnosed by the diagnosis unit 800 is transmitted to the image display device 940 installed in the operation management room 900 as the image display information 10 via the external output interface 220 and displayed. .
  • the operator in the operation management room 900 is notified of the diagnosis result for the operation state of the plant 100.
  • normal data for creating a diagnostic model is appropriately extracted from measurement signals obtained by measuring various state quantities of the plant 100, and a normal model is constructed.
  • the state change can be detected. That is, it is possible to detect the sign state before the occurrence of an abnormality and notify the operator that the state of the plant has changed at an early stage.
  • the diagnostic device information 50 stored in the measurement signal database 310 and the diagnostic model database 320 installed in the diagnostic device 200 can be arbitrarily displayed on the image display device 940 in the operation management room 900. These pieces of information can be corrected by an external input signal 2 generated by operating the external input device 910.
  • the operation mode of the diagnostic apparatus 200 has two types, a learning mode and a diagnostic mode.
  • the learning mode the learning unit 400 is operated to generate model information 7 and the model information 7 is stored in the diagnostic model database 320.
  • the diagnosis unit 800 is operated to generate the diagnosis result 9, and the image display information 10 included in the diagnosis result 9 is transmitted to the image display device 940 of the operation management room 900, whereby the operation state of the plant is displayed as an image. Display on device 940.
  • FIG. 2A is a flowchart showing the basic operation of the plant diagnosis apparatus shown in FIG.
  • the basic operation of the diagnostic apparatus 200 is executed by combining steps 1000, 1010, and 1020.
  • step 1000 it is determined whether the operation mode of the diagnostic apparatus 200 is a learning mode or a diagnostic mode. Then, in the case of the learning mode, the process proceeds to step 1010, and in the diagnosis mode, the process proceeds to step 1020.
  • step 1010 the learning unit 400 is operated to generate model information 7 by the model construction unit 700, and the created model information 7 is stored in the diagnostic model database 320.
  • Step 1020 the diagnosis unit 800 is operated to generate a diagnosis result 9 for the measurement signal 4 that is the operation state of the plant 100, and the image display information 10 including the generated diagnosis result 9 is transmitted to the image display device 940. As a result, the operation state of the plant 100 is displayed on the image display device 940.
  • the timing for operating the learning mode and the diagnostic mode of the diagnostic apparatus 200 can be arbitrarily designated by the operator.
  • various examples of timings at which the learning mode and the diagnostic mode are operated will be described using FIGS.
  • diagnosis is performed by operating both the learning mode and the diagnostic mode at every sampling period of the measurement signal. Diagnosis using the latest model is always possible by updating the diagnosis model every time the measurement signal is acquired.
  • the normal state learning mode can be operated every predetermined set period, and only the diagnostic mode can be operated every sampling period for diagnosis. .
  • the diagnosis mode is executed every sampling period, and the state of the plant can be diagnosed online.
  • the learning mode and the diagnostic mode can be operated at arbitrary timings by inputting an external input signal 2 for the operator to perform learning and diagnosis to the diagnostic device 200. . That is, it becomes possible to diagnose the operation state of the plant 100 by changing various conditions.
  • ART adaptive resonance theory
  • an implementation example of the data classification function in the classification unit that constructs the learning means in the plant diagnosis apparatus shown in FIG. 1 is configured by the data preprocessing device 510 and the ART module 520. To do.
  • the data preprocessing device 510 converts the operation data into input data for the ART module 520.
  • the data preprocessing device 510 and the ART module 520 are installed in the classification unit 500 of the learning unit 400.
  • the normalization method will be described by taking the process amount xi of the plant as an example.
  • Nxi (n) the number of data of xi is N and the nth measurement value is xi (n). Further, if the maximum value and the minimum value in N pieces of data are Max_i and Min_i, respectively, normalized data Nxi (n) is expressed by the following formula (1).
  • Nxi (n) ⁇ + (1 ⁇ ) ⁇ (xi (n) ⁇ Min_i) / (Max_i ⁇ Min_i) (1)
  • ⁇ ⁇ + (1 ⁇ ) ⁇ (xi (n) ⁇ Min_i) / (Max_i ⁇ Min_i) (1)
  • input data Ii (n) including data Nxi (n) and CNxi (n) is input to the ART module 520.
  • the above procedure is included in the input data conversion processing of the operation data to the ART module 520 performed in the data preprocessing device 510.
  • the measurement signal 4 of the plant 100 as input data is classified into a plurality of categories.
  • the ART module 520 includes an F0 layer 521, an F1 layer 522, an F2 layer 523, a memory 524, and a selection subsystem 525, which are coupled to each other.
  • the F1 layer 522 and the F2 layer 523 are connected via a weighting factor.
  • the weighting factor represents the prototype (prototype) of the category into which the input data is classified.
  • the prototype represents a representative value of the category.
  • Process 1 The input vector is normalized by the F0 layer 521, and noise is removed.
  • a suitable category candidate is selected by comparing the input data input to the F1 layer 522 with the weighting factor.
  • Process 3 The validity of the category selected by the selection subsystem 525 is evaluated by the ratio with the parameter ⁇ . If it is determined to be valid, the input data is classified into the category, and the process proceeds to process 4. On the other hand, if it is not judged to be valid, the category is reset, and an appropriate category candidate is selected from the other categories (repeat processing 2). Increasing the value of parameter ⁇ makes the category classification finer, and decreasing the value of ⁇ makes the classification coarse. This parameter ⁇ is referred to as a vigilance parameter.
  • Process 4 When all the existing categories are reset in Process 2, it is determined that the input data belongs to the new category, and a new weighting factor representing the prototype of the new category is generated.
  • weight coefficient WJ (new) corresponding to category J uses past weight coefficient WJ (old) and input data p (or data derived from input data). And updated by the following equation (3).
  • WJ (new) Kw ⁇ p + (1-Kw) ⁇ WJ (old) (3)
  • Kw is a learning rate parameter (0 ⁇ Kw ⁇ 1), and is a value that determines the degree to which the input vector is reflected in the new weighting factor.
  • equations (3) and equations (4) to (14) described below are incorporated in the ART module 520.
  • the characteristic of the data classification algorithm of the ART module 520 is the processing 4 described above.
  • process 4 when input data different from the learned pattern is input, a new pattern can be recorded without changing the recorded pattern. Therefore, it is possible to record a new pattern while recording a pattern learned in the past.
  • FIG. 3B is a block diagram showing the configuration of the F0 layer 521.
  • the input data is normalized again at each time, and a normalized vector u i 0 to be input to the F1 layer 521 and the selection subsystem 525 is created.
  • w i 0 is calculated from the input data I i according to the equation (4).
  • a is a constant.
  • x i 0 obtained by normalizing w i 0 is calculated using equation (5).
  • is a symbol representing the norm.
  • Equation 6 is a constant for removing noise. Since the minute value becomes 0 by the calculation of the equation (6), noise of the input data is removed.
  • FIG. 3C is a block diagram showing the configuration of the F1 layer 522.
  • u i 0 obtained by the equation (7) is held as a short-term memory, and p i input to the F2 layer 522 is calculated.
  • the formulas for calculating the F2 layer are collectively shown in formulas (8) to (14). However, a and b are constants, f () is a function shown in Expression (6), and T j is a fitness calculated by the F2 layer 522.
  • FIG. 4A is a block diagram showing a thermal power plant that is an embodiment of the plant 100.
  • the thermal power plant 100 includes a gas turbine generator 110, a control device 120, and a data transmission device 130.
  • the gas turbine generator 110 includes a generator 111, a compressor 112, a combustor 113, and a turbine 114.
  • the air sucked by the compressor 112 is compressed into compressed air, and this compressed air is sent to the combustor 113 and mixed with fuel for combustion.
  • the turbine 114 is rotated using the high-pressure gas generated by the combustion, and the generator 111 generates power.
  • the control device 120 controls the output of the gas turbine generator 110 according to the power demand.
  • the control device 120 uses the operation data 102 measured by a sensor (not shown) installed in the gas turbine generator 110 as input data.
  • the operation data 102 is state quantities such as intake air temperature, fuel input amount, turbine exhaust gas temperature, turbine rotation speed, generator power generation amount, turbine shaft vibration, and the like, and is measured at each sampling period. It also measures weather information such as atmospheric temperature.
  • the control device 120 calculates a control signal 101 for controlling the gas turbine generator 110 using these operation data 102. In addition, the control device 120 performs processing for generating an alarm when the value of the operation data 102 deviates from a preset range.
  • the alarm signal is processed as a digital signal of 1 when the operation data 102 deviates from a preset range, and 0 when within the range. When the alarm signal is 1, the operator is notified of the content of the alarm by sound or screen display.
  • the signal data transmission device 130 transmits the operation data 102 measured by the control device 120, the control signal 101 calculated by the control device 120, and the measurement signal 1 including the alarm signal to the diagnosis device 200.
  • FIG. 4B is a diagram for explaining the result of classifying the measurement signal 1 acquired from the plant 100 into categories.
  • the horizontal axis is time, and the vertical axis is measurement signal and category number.
  • FIG. 4 (c) is a diagram showing an example of the result of further classifying the measurement signal 1 of the plant 100 based on the result of classification into categories as shown in FIG. 4 (b).
  • FIG. 4 (c) as an example, two items of the measurement signal are displayed and represented by a two-dimensional graph.
  • the vertical axis and the horizontal axis indicate the measurement signals of the respective items normalized.
  • Measured signals are divided into a plurality of categories 550 (circles shown in FIG. 4C) by the ART module 520 in FIG. One circle corresponds to one category.
  • the measurement signals are classified into four categories.
  • Category number 1 is a group in which item A has a large value and item B has a small value
  • category number 2 is a group in which both items A and B have small values
  • category number 3 has a small value in item A
  • item B A group with a large value of
  • category number 4 is a group with a large value for both items A and B.
  • the classification unit 500 classifies the measurement signals into categories by paying attention to the similarity of the measurement signals.
  • This embodiment is characterized in that the period of normal state, predictive state, and abnormal state is determined based on the result of classifying measurement signals into categories as described above.
  • the operation of the period determination unit 600 that determines these periods will be described.
  • FIG. 5A is a diagram for explaining a first implementation example of the period determination unit 600 constituting the learning unit 400.
  • a graph 612 is a time-dependent change of the category into which the measurement signal of the plant 100 is classified.
  • Graphs 613 and 614 are changes over time in the occurrence ratio of a category in which the operation state of the plant 100 is not normal. That is, it can be determined that the operation state of the plant 100 has changed as the values of the graphs 613 and 614 are larger. In a present Example, when larger than the threshold value shown in FIG. 5, it shall determine with the driving
  • Graph 613 shows the results when the boundary between the normal state and the predictive state is set at time t1 for the operating state of the plant 100. That is, the category number that occurred before time t1 is compared with the category number that occurred after time t1, and the larger the number, the larger the value.
  • Graph 614 shows the results when the boundary between the normal state and the predictive state is set at time t2 for the operating state of the plant 100.
  • the third category 615 that is not in the normal state has occurred after time t1, but the second category in the normal state thereafter. 616 has occurred.
  • the reason why the third category is generated is not an influence of an abnormality or a sign thereof, but an influence of noise of a measurement value or the like. Therefore, it is considered that the period from time t1 to time t2 is in a normal state.
  • the boundary between the normal state and the predictive state is set at time t2 (see graph 614) with respect to the operation state of the plant 100, the period from time t2 to time t3 when an alarm for notifying the occurrence of abnormality is normal.
  • the occurrence rate of the category that is not in the state changes in a state higher than the threshold. That is, it is considered that the operation state of the plant has changed at time t2.
  • time t2 is the boundary time between the normal state and the predictive state
  • the period before time t2 is the normal state
  • the period from time t2 to the alarm occurrence is the predictive state.
  • the period determining unit 600 operates to determine the time t2 as the boundary between the normal state and the predictive state. Further, the time t3 when the alarm is generated is determined as the boundary between the predictive state and the abnormal state.
  • FIG. 5 (b) An algorithm for determining the boundary time between the normal state and the predictive state and the boundary time between the predictive state and the abnormal state with respect to the operating state of the plant 100 described with reference to FIG. 5 (a) is shown in FIG. 5 (b). Therefore, a method for determining the boundary time using the algorithm shown in FIG. 5B will be described below.
  • the present algorithm is executed by combining Steps 601, 602, 603, 604, 605, and 606.
  • step 601 which is processing in the period determining unit 600 of the learning unit 400, an initial value t (0) of a boundary time between a normal state and a predictive state is set.
  • This initial value is an arbitrary time, and is determined using, for example, a random number.
  • the time ta when the alarm is generated is set as a boundary time between the predictive state and the abnormal state.
  • step 602 the minimum value N of the occurrence ratio of categories that are not in a normal state during the period from time t (0) to time ta is calculated.
  • Step 603 N is compared with the threshold value ⁇ , and the boundary time between the normal state and the predictive state is updated using Equation (15) and Equation (16).
  • n is the number of repetitions of steps 603 to 606 in this flowchart, and ⁇ is a constant.
  • step 604 the minimum value N of the occurrence ratio of categories that are not in a normal state during the period from time t (n + 1) to time ta is calculated.
  • step 605 when N> ⁇ , the time t (n + 1) is held as the boundary time between the normal state and the predictive state.
  • step 606 if the number of repetitions n is less than or equal to a predetermined upper limit of the number of repetitions, n is changed to n + 1 and the process proceeds to step 603. If n exceeds the upper limit value, the process ends.
  • the boundary time ta between the predictive state and the abnormal state determined at step 601 and the boundary time t between the normal state and the predictive state held at step 605 are obtained.
  • the time ta is set as the boundary time between the normal state and the predictive state, and it is processed that there is no period of the predictive state.
  • the period information 6 configured with the time determined in this way is transmitted from the period determination unit 600 to the model construction unit 700.
  • FIG. 6A is a diagram illustrating a second implementation example of the period determining unit 600 constituting the learning unit 400, and is an explanatory diagram illustrating a situation where data is divided into sections at arbitrary time intervals. .
  • the period determining unit 600 of the learning unit 400 first divides data into sections at arbitrary time intervals as shown in FIG.
  • FIG. 6B is an explanatory diagram showing the appearance rate E of the category for each divided section shown in FIG. 6A.
  • the result classified by the classification unit 500 of the learning unit 400 is used.
  • the appearance rate E of the category is obtained for each section in which the data is divided as shown in FIG.
  • the degree of change V is calculated by Expression (17) using the immediately preceding section and the category appearance rate E.
  • E (p, m) means the appearance rate of the category number m in the interval p
  • ⁇ means calculating the sum when the category number m is changed from 1 to the maximum value of the category number.
  • FIG. 6C is an explanatory diagram showing a result of calculating the degree of change V for each of the divided sections shown in FIG.
  • the degree of change V increases. That is, a large change degree V means that the operating state of the plant is changing. Therefore, a change in the plant state can be detected using the degree of change V as an index.
  • the time t13 when the degree of change V exceeds the threshold is set as the boundary time between the normal state and the predictive state. Further, the boundary time between the predictor state and the abnormal state is determined by the method described with reference to FIGS. 5 (a) to 5 (b).
  • the boundary time between the normal state and the predictor state and the boundary time between the predictor state and the abnormal state are automatically obtained, and the normal period, the predictor period, and the abnormal period are determined. Since it is not necessary for the operator to set these periods, the labor required for applying the diagnostic apparatus can be reduced.
  • FIG. 7 is a diagram for explaining the operation of the diagnostic means 800 in the plant diagnostic apparatus of the present embodiment shown in FIG.
  • the diagnostic means 800 uses which model the measurement signal belongs to. To determine the operating state of the plant.
  • data points 791 and 792 are examples of results obtained by plotting the measurement signals.
  • the data point 791 belongs to the sign model 770, the data point 791 is diagnosed as being in the “predictive state”.
  • the data point 792 does not belong to any model, it is diagnosed as “an unknown state that has not been experienced in the past”.
  • the diagnosis unit 800 can diagnose the operation state of the plant as one of a normal state, a precursor state, an abnormal state, and an unknown state.
  • diagnostic means 800 when operating the diagnostic means 800, it is determined only whether or not it belongs to the normal model, and if it does not belong to the normal model, it can be diagnosed as an unknown state.
  • An unknown state means that the state of the plant has changed because the state of the plant is not normal.
  • a normal model is constructed and diagnosed using measurement signals excluding data in the period of abnormal state and predictive state. For this reason, if a sign of abnormality appears in the measurement signal, a new category is generated, and a change in the state of the plant can be detected.
  • the distance 792a between the data point and the normal model 760, the distance 792b between the predictive model 770, and the distance 792c between the abnormal model 780 are compared. And it classify
  • FIG. 8 (a), 8 (b), and 8 (c) show two modes of data stored in the measurement signal database 310 and the diagnostic model database 320 in the plant diagnosis apparatus shown in FIG. Each is shown. These figures can be displayed on the image display device 940 of FIG.
  • the value of the measurement signal 1 (data items A, B, and C are shown in the figure) that is operation data measured for the plant 100 is sampled. Stored for each period (time on the vertical axis).
  • scroll boxes 312 and 313 that can be moved vertically and horizontally on the display screen 311, a wide range of data can be scroll-displayed.
  • 8 (b) and 8 (c) are diagrams for explaining modes of data stored in the diagnostic model database 320 of FIG.
  • FIG. 8B shows the relationship between the category number and the weighting factor in the diagnostic model database (1)
  • FIG. 8C shows the category in which the time in the diagnostic model database (2) and the data at that time are classified. Relations with numbers are stored and displayed as screens 321 and 325, respectively.
  • the weighting factor is the center coordinate of the category. As shown in FIG. 8B, the weighting coefficient is created for each normal model (322a, 322b), predictive model (322c), and abnormal model (322d).
  • FIGS. 9A and 9B are diagrams for explaining screens of plant diagnosis results displayed on the image display device 940 installed in the operation management room 900 in the plant diagnosis apparatus shown in FIG. .
  • FIG. 9A is an explanatory diagram in which diagnosis results (plant operation states) are displayed in a list on the image display device 940. As shown in FIG. 9A, plant diagnosis results (plant operation states). ) Is displayed as a list and the operator is notified of the diagnosis result. Thereby, the operator can grasp
  • FIG. 9B shows the boundary time tA between the normal state and the predictive state, and the boundary time tB between the predictive state and the abnormal state determined by the period determining unit constituting the learning means provided in the plant diagnosis apparatus shown in FIG.
  • a default value as shown in FIG. 9 (b), the boundary time tA between the normal state and the predictive state determined by the period determining unit 600 constituting the learning unit 400 in the plant diagnosis apparatus, The boundary time tB between the predictive state and the abnormal state is displayed as a default value.
  • the period of normal state, predictive state, and abnormal state can be corrected by using the external input signal 2 generated by operating the external input device 910 including the keyboard 920 and the mouse 930. That is, on the screen 942, the corrected time is input using the keyboard 920 in the column 943a where the boundary time between the normal state and the predictive state is displayed, and the column 944a where the boundary time between the predictive state and the abnormal state is displayed.
  • the period information 6 can be corrected by pressing the button.
  • the period information 6 may be corrected by dragging the boundary line 943b between the normal state and the predictive state and the boundary line 943b between the predictive state and the abnormal state with the mouse 930 to correct the position of the line and pressing the execution button. It can. There is a possibility that the accuracy of diagnosis can be improved by determining the period information 6 based on the abundant experience of the operator.
  • the diagnostic apparatus according to the present embodiment can perform the above-described correction.
  • each of the above-described configurations, functions, processing units, processing means, etc. may be realized by hardware by designing a part or all of them with an integrated circuit.
  • Each of the above-described configurations, functions, and the like may be realized by software that interprets and executes a program that realizes each function.
  • Information such as programs, tables, files, measurement values, and calculation information for realizing each function can be stored in a storage device such as a memory or a hard disk, or a storage medium such as an IC card, an SD card, or a DVD. Therefore, each process and each configuration can be realized as a processing unit or a program module.
  • information lines indicate what is considered necessary for the explanation, and not all control lines and information lines on the product are necessarily shown. In fact, you may think that almost all welfare is connected to each other. According to the present embodiment, it is possible to realize a plant diagnostic apparatus and a plant diagnostic method that can detect a sign state in the middle of a plant state from a normal state to an abnormal state. As a result, a plant diagnostic apparatus and a plant diagnostic method capable of detecting a change in the state of the plant at an early stage before the plant reaches an abnormal state are obtained.
  • the present invention can be widely applied to various plants as a plant diagnosis apparatus and a plant diagnosis method.

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Abstract

L'invention porte sur un dispositif de diagnostic d'installation apte à détecter une pré-alarme du fait que l'installation est dans le passage d'un état normal à un état anormal. Le dispositif de diagnostic d'installation diagnostique l'état de fonctionnement de l'installation sur la base d'un signal de mesure dans lequel une quantité d'état est mesurée à partir de l'installation, et affiche le résultat de diagnostic sur un dispositif d'affichage d'image. Le dispositif de diagnostic d'installation comporte : un moyen d'apprentissage pour construire un modèle utilisé dans le diagnostic à l'aide du signal de mesure qui mesure la quantité d'état de l'installation ; et un moyen de diagnostic pour diagnostiquer l'état de fonctionnement de l'installation à l'aide du modèle construit par le moyen d'apprentissage. Le moyen d'apprentissage a une unité de catégorisation qui catégorise des données avec des valeurs similaires dans la même catégorie ; une unité de détermination de période qui détermine la période normale, la période de pré-alarme et la période anormale par estimation de la différence dans des tendances dans le signal de mesure sur la base du résultat de catégorisation de l'unité de catégorisation ; et une unité de construction de modèle qui construit un modèle normal à l'aide de signaux de mesure dans la période normale. Le dispositif de diagnostic d'installation est configuré de telle sorte que le moyen de diagnostic détermine si le signal de mesure au temps actuel est catégorisé ou non dans le modèle normal construit par l'unité de construction de modèle ; et, dans le cas où le signal de mesure au temps actuel se trouve à l'intérieur du modèle normal, qu'il diagnostique que l'installation est dans l'état normal, ceci étant affiché sur le dispositif d'affichage d'image ; et, dans le cas où le signal de mesure au temps actuel n'est pas à l'intérieur du modèle normal, qu'il diagnostique que l'installation est dans un état non connu non expérimenté précédemment, ceci étant affiché sur le dispositif d'affichage d'image.
PCT/JP2010/007025 2010-12-02 2010-12-02 Dispositif de diagnostic d'installation et procédé de diagnostic d'installation WO2012073289A1 (fr)

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