JPH08304125A - Plant diagnosing apparatus - Google Patents

Abstract

PURPOSE: To set and change analysis parameters in correspondence with the state of a plant. CONSTITUTION: A frequency analysis part 103 uses the data fetched by a data input part 102, performs the frequency analysis on line by the analysis parameters comprising the specified sampling period and the specified data number and outputs the on-line frequency analysis data. A comparing part 104 compares the on-line frequency analysis data and the normal-time frequency analysis data and outputs the result of the comparison. A judging part 10 judges the state of a plant based on the result of the comparison. A judged-result notifying part 107 notifies the outside of the judged-result data. An analysis- parameter indicating part 108 fetches the judged-result data and sets and changes the analysis parameters, which are analyzed by the frequency analysis part 103, in correspondence with the judged-result data.

Description

Detailed Description of the Invention

[0001]

[Field of Industrial Application] The present invention is installed in various power plants such as nuclear power and thermal power plants and chemical and steel plants.
The present invention relates to a plant diagnostic device that monitors and diagnoses the state of the plant.

[0002]

2. Description of the Related Art Nuclear power plants, thermal power plants and chemical plants
Steel and other plants are of great social importance, and careful consideration is required for their operation and management. In order to operate and manage the plant safely and efficiently, it is necessary to monitor whether each part of the plant is performing its function properly,
Appropriate operation must be performed according to the condition of the plant. In addition, if a failure occurs in any part of the plant and it falls into an abnormal state, appropriate operation should be performed to prevent the plant from falling into a more serious abnormal state depending on the content of the abnormality. It is necessary to take appropriate action depending on the situation.

In such plant operation and management, a person interprets and judges based on signals obtained from various parts of the plant, and carries out a driving operation according to the situation. conventionally,
The signals collected by the sensors installed in each part of the plant were transmitted to the central operating room by a cable, and the signals were presented to the operator by an indicator or recorder in the central operating room. Operators read the process status of each part of the plant from the indicator and recorder, interpret and judge the contents, and performed appropriate response operations.

In recent years, these plants have become large in scale, and the range to be monitored in order to operate and manage the plants has become wide. Therefore, the number of data required for operation and management will increase, and in the management based on indicators and recorders,
One person has to monitor too much (a lot of) data.

For these reasons, a computer has been widely introduced to support the operation and monitoring of the plant as the plant becomes larger. The data of the sensors installed in each part of the plant are transmitted via a cable and then input to the computer in parallel with the indicator and recorder. The computer automatically processes and interprets the captured data and outputs it as an appropriate signal according to its content.

The functions of the computer in such a system include monitoring support and driving support. The monitoring support function is a function of processing a signal input to a computer into a form that is easily understood by humans and outputting the signal to the outside through a CRT display device or the like. The operation support function is an advanced function of the input signal processing, which determines the operation operation to be performed next from the state of each part of the plant and outputs the result to the outside. As a form of output to the outside, a guide message is output to a CRT display device or the like, and the actual driving is performed by a human. There is also a system in which a signal output from a computer directly activates actuators in various parts of the plant to automatically operate the plant.

In addition to such functions related to monitoring and operation, a system has been introduced in which a computer has a function of acting as a substitute for human activities related to plant management. This is a function to detect the abnormal state when a failure occurs in any part of the plant and the plant falls into an abnormal state, interpret the contents of the abnormality and identify the failure point. .

From the social importance of the plant, it is necessary to suppress the occurrence of a failure as much as possible. However, if a failure still occurs, the failure occurrence should be detected as early as possible and the location And the cause must be identified and appropriate corrective action must be taken promptly. It is a system that executes functions to support such activities. Such a system is called a plant diagnostic system. The plant diagnosis system has a configuration in which the signals of sensors installed in various parts of the plant can be taken in through a transmission line. At this time, the type of signal to be captured is selected so as to reflect the sign of an abnormality that has occurred in the plant.

The plant diagnostic system inputs sensor signals transmitted from various parts of the plant online in time series, performs specific processing on the obtained data, and interprets / determines the contents thereof. . Normally, the obtained time-series data is periodically processed at a constant cycle, and when a sign of abnormality is detected, the fact is notified to the outside.

In such a plant diagnosis system, noise analysis is one of the techniques used for signal analysis for diagnosis. The noise analysis is a method in which a change in a time-series signal of a process in each part of a plant is detected as fluctuation (noise), and a frequency analysis is performed on the signal to analyze what frequency characteristic the noise has. This is a method that focuses on the fact that when an abnormality occurs in any part of the plant, a different type of change (fluctuation) from the normal appears due to the abnormality. In diagnosis, an abnormality is detected by detecting the special component of such fluctuation.

In the frequency analysis, it is assumed that the fluctuation waveform of a signal is represented by the composition of standing waves having different frequencies and independent from each other, and the target waveform has a difference in power and phase between waves of each frequency. It is to analyze whether it is a synthesized one. The frequency analysis result for the target signal is obtained by dividing the frequency band of the analysis range by a constant number defined by the analysis algorithm, and for each frequency component obtained, the absolute value representing the power and the angle representing the phase difference (that is, , Represented by a vector) is obtained.

There are several analysis methods as such a frequency analysis method, and the Fourier transform method is often used in an actual system. The Fourier transform is a method of analysis using a sine wave as the standing wave, and is obtained by the following algorithm.

A method of converting the function g (t) of time t into a function of the function of angular frequency ω by the following conversion formula (1) is called Fourier transform.

[0014]

[Equation 1]

By the Fourier transform, a function (waveform) relating to time is converted into a continuous function of angular frequency. When this calculation is executed by a digital computer, an FF that converts (digitizes) the waveform data into discrete data by sampling and discretely performs the processing based on the above conversion formula.
The conversion is executed by an algorithm such as T (Fast Fourier Transform).

In the plant diagnosis system based on the frequency analysis by such a Fourier transform, process signals suitable for diagnosing the state of the plant are selected from the signals input to the computer, and their time series data are selected. Is taken in at a constant cycle and frequency analysis is performed by Fourier transform. As described above, as a result of the frequency analysis, the power and phase angle for each frequency component obtained by dividing the frequency band of the analysis range by a fixed number defined by the analysis algorithm are obtained.

In normal diagnosis, power data (power spectral density) is used from these results. By using only the power data from the frequency analysis result, a two-dimensional pattern having a frequency dimension and a power spectral density dimension can be obtained. The system has a frequency analysis result of a normal signal as a reference, and compares and collates with the analysis result at the time of executing diagnosis, and if a large deviation occurs, it is determined that an abnormality has occurred.

The following algorithm is used as an algorithm for determining an abnormality.

(1) The difference between the normal pattern and the pattern at the time of diagnosis is taken for each frequency, and the difference is added over the entire analysis frequency band. If the sum is within a certain range, it is normal. And the method to judge as abnormal when it exceeds the range.

(2) A method of taking the square of the difference between the normal pattern and the pattern at the time of execution of diagnosis for each frequency and making a determination by the same method as above.

(3) A method of judging by a combination of the above two.

The plant diagnostic device based on the frequency analysis as described above basically has a configuration as shown in FIG.

The signals detected by the sensors installed in each part of the plant are transmitted to the data input section (process input / output device) 22 of the computer through the data transmission line 21. Therefore, it is converted into a digital signal so that it can be processed by a computer at a constant sampling period. The signals converted into digital signals are sequentially sent to the frequency analysis unit 23 in time series, and frequency analysis is performed by signal processing such as Fourier analysis.

The result analyzed by the frequency analysis unit 23 is sent to the comparison processing unit 24, where it is compared and collated with the data of the normal frequency characteristic pattern stored in the normal frequency characteristic database 25 in advance. The result of comparison processing is sent to the determination unit 26, and the determination result of normality / abnormality is determined there, and the determination result of normality / abnormality is transmitted from the determination result notification unit 27 to the outside.

In such frequency analysis, the frequency band (range) to be analyzed and the resolution have important meanings. That is, the characteristics of the process signal to be diagnosed must be analyzed in advance, and the frequency band in which the characteristics appear must be within the analysis target range.

Further, even if the analysis target range is too large for the frequency band of interest, the characteristic of the target frequency band cannot be obtained. Therefore, it is necessary to set an appropriate analysis target range according to the purpose of analysis and the characteristics of the analysis target signal.

Furthermore, the resolution of the frequency at which the result of the analysis is obtained must be set appropriately, and the characteristic of the characteristic frequency of the target signal must be sufficiently separated from other frequencies to be manifested. . If the analysis is performed with a too coarse resolution, the characteristics of the characteristic frequency may be buried. In addition, if the analysis is performed with a very fine resolution, a slight shift in the characteristic frequency may have a great influence on the determination of the diagnosis, and the determination may not be performed correctly.

For this reason, in frequency analysis, the frequency band and resolution to be analyzed must be set appropriately according to the purpose of analysis and the characteristics of the target signal.

In the Fourier transform used for the normal frequency analysis described above, the frequency band to be analyzed is determined by the sampling frequency of the target signal, and the resolution of the analysis is determined by the number of signals used in the analysis. That is, the frequency of 1/2 of the sampling frequency becomes the analysis range, and the resolution of analysis is set in proportion to the number of signals used for analysis, and the higher the number of signals, the higher the resolution.

In the conventional plant diagnosis system, the frequency band and the resolution of the frequency analysis are set according to the characteristics of the process signal used for diagnosis, and the diagnosis is performed by fixing the sampling period of the signal and the signal used for analysis according to the frequency band and resolution. Was there.

Taking a diagnostic system for a boiling water nuclear power plant (BWR) as an example, a diagnosis based on noise analysis is performed when the following sampling period and the number of data points used for one frequency analysis are set. There is.

Core signal (in-core neutron flux signal: local neutron region monitor signal, etc.) Sampling period: 50 msec Time series score of one analysis: 1024 points (frequency band 0.
(0195-10Hz)

Signals of control system (water supply control system, recirculation control system, pressure control system, etc.) Sampling period: 100 msec Time series score of one analysis: 1024 points (frequency band 0.
(00977-5Hz)

Signals related to jet pump Sampling cycle: 50 msec Time series score of one analysis: 256 points (frequency band 0.0
78 to 10 Hz)

[0035]

However, in the conventional plant diagnostic device, the sampling period of the signal used for frequency analysis and the number of signals used for analysis are fixed, so that flexible diagnosis is impossible. However, the content that can be diagnosed is fixed to one type, and there is a problem that detailed state analysis cannot be performed.

The present invention has been made in consideration of such a situation, and its purpose is to perform detailed diagnosis by changing the conditions of analysis / diagnosis according to the state of the plant. It is also an object of the present invention to provide a plant diagnostic device that can be performed and can perform detailed diagnostics offline if any abnormality is found by online diagnostics.

[0037]

The invention of claim 1 comprises a data input section for taking in plant data, and a predetermined sampling period and a predetermined number of data using the data taken in by this data input section. Frequency analysis part that performs online frequency analysis based on analysis parameters and outputs online frequency analysis data; normal frequency characteristic database that saves frequency analysis results when the plant is normal as normal frequency analysis data; online frequency analysis Plant diagnosis having a determination unit that compares the data and the normal frequency analysis data to determine the state of the plant and outputs the determination result data, and a determination result notification unit that notifies the determination result data by this determination unit to the outside. In the device, take the judgment result data from the judgment unit and analyze the frequency according to the judgment result data. There is obtained so as to provide an analysis parameter instructing unit for changing setting of the analysis parameters to be analyzed.

According to a second aspect of the present invention, in the plant diagnosis apparatus according to the first aspect, whether or not the decision result data obtained by the decision unit is taken in and the plant data input by the data input unit based on the decision result data is stored is determined. A data collection instructing unit that determines whether or not to store the plant data when the data collection instructing unit determines to store the plant data is added to the data storage processing unit that captures the plant data from the data input unit and stores it in the plant database. It is a thing.

According to a third aspect of the present invention, the frequency is input online by using a data input section for taking in plant data, and an analysis parameter consisting of a predetermined sampling period and a predetermined number of data using the data taken in by this data input section. A frequency analysis unit that performs analysis and outputs online frequency analysis data, a normal frequency characteristic database that saves frequency analysis results when the plant is normal as normal frequency analysis data, online frequency analysis data and normal frequency analysis In the plant diagnostic device having a determination unit that compares the data and determines the state of the plant and outputs the determination result data, and the determination result notification unit that notifies the determination result data by this determination unit to the outside, from the determination unit Analysis that the frequency analysis unit analyzes according to the judgment result data And analyzing the parameter instructing unit that sets change parameters, we take the decision result data by the judging unit,
Based on this determination result data, a data sampling instruction unit that determines whether to save the plant data input by the data input unit, and when the data sampling instruction unit determines to store the plant data, the data input unit Feature data from the time-series frequency analysis result data obtained by repeatedly performing frequency analysis using the primary analysis parameters for the data storage processing unit that captures plant data and stores it in the plant database and the plant data stored in the plant database And a primary analysis unit for extracting the time-series data of.

According to the invention of claim 4, in the plant diagnosis apparatus according to claim 3, detailed analysis is performed by the secondary analysis parameter based on the time series data of the characteristic frequency obtained by the primary analysis unit. The secondary frequency analysis result data is determined and a secondary analysis unit for notifying the determination result to the outside is added.

According to a fifth aspect of the present invention, in the plant diagnostic apparatus according to the third aspect, the primary analysis unit sets a primary analysis parameter setting unit for setting a primary analysis parameter having a wide frequency band as an analysis condition, and the primary analysis parameter setting. The time-series frequency analysis unit that executes the iterative frequency analysis using the plant data in the plant database with the primary analysis parameters set by the unit and outputs the time-series frequency analysis result data, and the time-series frequency analysis unit. And an analysis result time series data extraction unit for extracting time series data of characteristic frequencies from the time series frequency analysis result data.

According to a sixth aspect of the present invention, in the plant diagnosis apparatus according to the fourth aspect, the secondary analysis unit sets a secondary analysis parameter for setting a secondary analysis parameter in which a frequency band is narrowed down to a characteristic frequency band as an analysis condition. Section, a secondary frequency analysis section that performs frequency analysis with the secondary analysis parameters set by this secondary analysis parameter setting section, and the state of the plant is determined by the frequency analysis data obtained by this secondary frequency analysis section. The secondary determination unit and the secondary determination result notification unit that notifies the determination result by the secondary determination unit to the outside are provided.

According to a seventh aspect of the present invention, in the plant diagnosis apparatus according to any one of the first to sixth aspects, the analysis parameter whose setting is changed by the analysis parameter instruction unit is either a sampling period or the number of data. One or both.

The invention according to claim 8 is the plant diagnostic apparatus according to claim 5, wherein the analysis parameters set by the primary analysis parameter setting unit are the sampling period and the number of data.

According to a ninth aspect of the present invention, in the plant diagnosis apparatus according to the sixth aspect, the secondary analysis parameters set by the secondary analysis parameter setting unit are a frequency band and the number of data.

[0046]

According to the plant diagnosis apparatus of the first aspect, the setting of the analysis parameter is changed according to the determination result data of the online frequency analysis. As a result, when the determination result data is abnormal, the frequency band to be analyzed or the number of data can be changed, so that detailed frequency analysis can be performed and online diagnosis can be executed according to the situation of the plant.

According to the plant diagnosing device of the second aspect, whether or not the plant data is stored in the plant database is determined by the online determination result data, and the plant data is stored in the plant database according to the determination result. Thereby, further detailed analysis can be performed by using the plant data stored in the plant database, and more detailed plant diagnosis can be performed.

According to the plant diagnosis apparatus of the third aspect, the frequency analysis is repeatedly executed on the plant data stored in the plant database using the primary analysis parameters, and the obtained frequency analysis result data is arranged in a time series. Time-series data of the characteristic frequency is extracted from the frequency analysis result data of. This allows frequency analysis to be narrowed down from time-series data of characteristic frequencies.

According to the plant diagnosis apparatus of the fourth aspect, the secondary frequency analysis is performed from the time series data of the characteristic frequency by using the secondary analysis parameter and the time series data of the characteristic frequency. As a result, it is possible to perform an analysis that grasps the change state of the time-series data of the characteristic frequency, and to make a more detailed diagnosis.

According to the plant diagnosis apparatus of claim 5, the frequency analysis result data obtained by arranging the frequency analysis result data obtained by repeating the frequency analysis using the plant data in the relatively frequency band as the analysis condition is arranged in time series. Time series data is obtained. Further, the time-series data of the characteristic frequency obtained by extracting the value of the characteristic frequency of the data from the time-series data of the frequency analysis result data is extracted. Thereby, more detailed diagnosis can be performed from the time-series data of the characteristic frequency.

According to the plant diagnosing device of the sixth aspect, frequency analysis can be performed by narrowing down to the characteristic frequency band, and detailed diagnosis can be performed.

According to the plant diagnosing device of the seventh aspect, the sampling period and the number of data can be set and changed as analysis conditions depending on the situation of the plant.

According to the plant diagnosing apparatus of the eighth aspect, the sampling period as the analysis parameter and the number of data are set according to the characteristics of the signal to be analyzed, and the iterative frequency analysis is performed. Is obtained.

According to the plant diagnosis device of the ninth aspect, detailed plant diagnosis can be performed because the frequency band and the number of data are set as analysis parameters according to the characteristics of the signal to be analyzed.

[0055]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram of a plant diagnostic device showing a first embodiment of the present invention.

The plant diagnostic device A includes a data input unit 10
2, a frequency analysis unit 103, a comparison processing unit 104, a normal frequency characteristic database 105, a determination unit 106, a determination result notification unit 107, and an analysis parameter instruction unit 108, and are connected to the data transmission line 101.

The data input unit 102 takes in plant data. The frequency analysis unit 103 uses the data captured by the data input unit 102 to perform online frequency analysis with an analysis parameter including a predetermined sampling period and a predetermined number of data, and outputs online frequency analysis data.

The normal frequency characteristic database 105 is
The frequency analysis result when the plant is normal is saved as normal time frequency analysis data. The comparison processing unit 104 compares the online frequency analysis data with the normal frequency analysis data and outputs a comparison result.

The judging section 106 judges the state of the plant from the comparison result and outputs the judgment result data. The determination result notifying unit 107 notifies the determination result data to the outside. The analysis parameter instruction unit 108 takes in the determination result data and changes the setting of the analysis parameter analyzed by the frequency analysis unit 103 according to the determination result data.

First, the signal from the sensor transmitted from each part of the plant is input to the data input unit 102 which is the process input / output device of the computer through the data transmission line 101. The data input unit 102 converts the input signal into a format (digital signal) that can be processed by a computer with a high-speed sampling period (for example, 1 msec) and takes in the data in time series. The data of the signal to be analyzed among the captured time-series digital signals is sent to the frequency analysis unit 103.

The frequency analysis unit 103 executes frequency analysis on the time-series data based on the above-described Fourier analysis algorithm or the like. When performing a Fourier analysis, the sampling period of the signal used for analysis and the number of data used for analysis have important meanings as described above. That is, the sampling period determines the frequency band (range) to be analyzed, and the number of data used for analysis determines the resolution of analysis.

The frequency analysis unit 103 analyzes these analysis parameters using the data designated by the analysis parameter designation unit 108. For example, when the analysis parameter instructing unit 108 is set to use data of 100 msec sampling (10 Hz) and analysis is performed using 256 pieces of data, the frequency analyzing unit 103 outputs 0 to 5 Hz.
The frequency analysis result obtained by dividing the band of 256 into 256 is obtained.

In this case, data of 100 msec sampling is collected by the data input unit 102 for 1 mse.
The c-sampling data can be obtained by thinning out every 100 data.

The result of the period analysis performed by the frequency analysis unit 103 is a two-dimensional pattern of frequency and power spectral density as shown in FIG.

The frequency analysis result thus obtained is sent to the comparison processing unit 104. In the comparison processing unit 104,
The sent data is compared with the data stored in the normal frequency characteristic database 105. The normal-time frequency characteristic database 105 is a database that stores process data of a normally collected plant. The comparison processing unit 104 reads out the normal frequency characteristic data of the same type as the target signal from the normal frequency characteristic database 105, and performs the following calculation for comparison.

(1) The difference between the normal pattern and the pattern at the time of diagnosis is taken for each frequency, and the values are added together over the entire analysis frequency band to calculate the sum.

(2) For each frequency, the square of the difference between the normal pattern and the pattern at the time of diagnosis is taken, and the values are added together over the entire analysis frequency band to calculate the sum.

Next, the result of the comparison process is sent to the determination unit 106. The determination unit 106 compares and determines whether or not the value of the result of the comparison process that has been sent is within a certain set range. If the value is within the set range, it is determined to be normal, and if the value deviates from the range, it is determined to be abnormal. .

Therefore, the result judged by the judging unit 106 is transmitted to the judgment result notifying unit 107, and the result is notified to the outside. As a form of the notification, a method of being presented as a message on a CRT display device, a method of automatically starting another device, or the like can be considered.

On the other hand, the result determined by the determination unit 106 is also transmitted to the analysis parameter instruction unit 108. In the analysis parameter designating unit 108, when it is determined from the results of the analysis so far that some abnormality has occurred,
In order to make the analysis in detail, the analysis conditions are adjusted with reference to a database (not shown). The sampling cycle and the number of data are described in the database as analysis conditions according to the analysis result. As a result, based on the result of the condition resetting in the analysis parameter instruction unit 108, the frequency analysis unit 103 performs analysis under the new condition and makes the same determination.

For example, when the number of data used for the analysis is changed to 512 or 1024 while the data having the same sampling period is used for the analysis performed with the data of 100 msec sampling and 256 data in the above example, the analysis is performed. Although the target frequency band remains 0 to 5 Hz, the resolution for dividing the range is high, and more detailed analysis is possible.

The sampling cycle of the target data is set to 1
0msec sampling (decimated data every 10)
When the conditions are set again so as to perform the analysis with 1 msec sampling or 1 msec sampling, the frequency bands to be analyzed are 0 to 50 Hz and 0 to 500 Hz, respectively, and the range of interest as the analysis target can be widened.

As described above, according to the first embodiment, the setting of the analysis parameter is changed according to the determination result data of the online frequency analysis. As a result, when the determination result data is abnormal, the frequency band to be analyzed or the number of data can be changed, so that detailed frequency analysis can be performed and online diagnosis can be executed according to the situation of the plant.

FIG. 3 is a block diagram of a plant diagnosis system showing a second embodiment of the present invention.

The plant diagnostic device B includes a data input unit 10
2, frequency analysis unit 103, comparison processing unit 104, normal frequency characteristic database 105, determination unit 106, determination result notification unit 107, analysis parameter instruction unit 108, data collection instruction unit 109, data storage processing unit 110, and plant database 111. And data transmission line 101
The data collection instructing unit 109, the data storage processing unit 110, and the plant database 111 are added to the first embodiment.

The data collection instructing unit 109 takes in the determination result data from the determining unit 106 and determines whether or not to save the plant data input by the data input unit 102 based on this determination result data. Data storage processing unit 110
When the data collection instructing unit 109 determines to store the plant data, the data input unit 102 takes in the plant data and stores it in the plant database 111.

Next, the operation of the second embodiment will be described.

First, the signals from the sensors transmitted from the various parts of the plant are input to the data input part 102 which is the process input / output device of the computer through the data transmission line 101 as in the first embodiment. In the data input unit 102, the input signal is output at a high sampling rate (for example, 1 mse).
In c), it is converted into a format (digital signal) that can be processed by a computer and is taken in time series. The data of the signal to be analyzed among the captured time-series digital signals is the frequency analysis unit 1.
Sent to 03.

In the frequency analysis unit 103, these analysis parameters are analyzed by the analysis parameter instruction unit 1 as in the first embodiment.
Analysis is performed using the data indicated by 08.

The result of the period analysis carried out by the frequency analysis unit 103 is a two-dimensional pattern of frequency and power spectral density as shown in FIG.

The frequency analysis result thus obtained is sent to the comparison processing unit 104. In the comparison processing unit 104,
The sent data is compared with the data stored in the normal frequency characteristic database 105.

Next, the result of the comparison process is sent to the determination unit 106. The determination unit 106 compares and determines whether or not the value of the result of the comparison process that has been sent is within a certain set range. If the value is within the set range, it is determined to be normal, and if the value deviates from the range, it is determined to be abnormal. .

Therefore, the result judged by the judging unit 106 is transmitted to the judgment result notifying unit 107, and the result is notified to the outside.

On the other hand, the result determined by the determination unit 106 is also transmitted to the analysis parameter instruction unit 108. In the analysis parameter designating unit 108, when it is determined from the results of the analysis so far that some abnormality has occurred,
Adjust the analysis conditions to make the analysis more detailed. As a result, based on the result of the condition resetting in the analysis parameter instruction unit 108, the frequency analysis unit 103 performs analysis under the new condition and makes the same determination.

Further, the result determined by the determination unit 106 is transmitted to the data collection instruction unit 109. The data collection instruction unit 109 determines whether to save the time-series data of the process signal based on the result determined by the determination unit 106, and when saving, instructs the data storage processing unit 110 to that effect. . In the normal processing, when it is determined that an abnormality has occurred, an instruction is given to save the data at that time.

The data storage processing unit 110 collects the time-series data of the process input by the data input unit 102 based on the instruction from the data collection instruction unit 109 and stores it in the plant database 111. Here, as the data to be stored, in order to analyze the history before and after the abnormality, the data after a certain period of time is collected from the data before a certain period of time from the time when the abnormality is determined and stored.

The data stored in the plant database 111 has an important meaning as a time series representing the state of the plant when an abnormality occurs in the plant, and is used for further detailed analysis of the content of the abnormality that has occurred in the plant. Is
Processing is performed on this data to perform analysis.

As described above, according to the second embodiment, whether or not the plant data is stored in the plant database is determined by the online determination result data, and the plant data is stored in the plant database according to the determination result. Thereby, further detailed analysis can be performed by using the plant data stored in the plant database, and more detailed plant diagnosis can be performed.

FIG. 4 is a block diagram of a plant diagnostic device showing a third embodiment of the present invention.

In the third embodiment, a plant diagnostic device is applied to a nuclear power plant, and the plant diagnostic device C is
The data input unit 102, the frequency analysis unit 103, the comparison processing unit 104, the normal frequency characteristic database 105, the determination unit 106, the determination result notification unit 107, the analysis parameter instruction unit 108, the data collection instruction unit 109, and the data storage processing unit 1
10 and a plant database 111, and a time series frequency analysis unit 112 which is a primary analysis unit 120.
And an analysis result time series data extraction unit 113, a primary analysis parameter setting unit 115, a secondary frequency analysis unit 114, which is a secondary analysis unit 121, and a secondary analysis parameter setting unit 1.
16, a secondary determination unit 117, and a secondary determination result notification unit 118, which are connected to the data transmission line 101, and a primary analysis unit 120 and a secondary analysis unit 121 are added to the second embodiment.

The primary analysis parameter setting unit 115 sets a primary analysis parameter having a wide frequency band as an analysis condition. The time-series frequency analysis unit 112 executes repeated frequency analysis using the plant data of the plant database 111 with the primary analysis parameters set by the primary analysis parameter setting unit 115, and outputs time-series frequency analysis result data. Analysis result time series data extraction unit 11
3 extracts time series data of characteristic frequencies from the time series frequency analysis result data obtained by the time series frequency analysis unit 112.

The secondary analysis parameter setting unit 116 sets the secondary analysis parameter in which the frequency band is narrowed down to the characteristic frequency band as the analysis condition. The secondary frequency analysis unit 114
The frequency analysis is executed by the secondary analysis parameter set by the secondary analysis parameter setting unit 116. The secondary determination unit 117 determines the state of the plant based on the frequency analysis data obtained by the secondary frequency analysis unit 114.
The secondary determination result notification unit 118 notifies the determination result of the secondary determination unit 117 to the outside.

Next, the operation of the third embodiment will be described.

Various parts of the nuclear power plant are provided with various sensors for monitoring the process of each part.

For example, the amount of neutrons inside the core of a nuclear reactor, the flow rate and pressure of liquid (water) flowing through pipes, valves and pumps,
Process quantities such as the liquid level of liquid (water) in various vessels such as nuclear reactors and the temperature of liquid (water) inside each plant part and inside are important monitoring items. These process quantities are effective as quantities that represent the internal state of the plant, and these quantities make it possible to grasp the internal state that cannot be seen even from the outside.

In addition to such analog values, pump stop / operation, valve open / close, switch contact O
Digital values representing binary states such as N / OFF and ON / OFF of a certain state are also important monitoring items.

First, a signal from a sensor transmitted from each part of the plant is input to a data input unit 102 which is a process input / output device of a computer through a data transmission line 101. The data input unit 102 converts the input signal into a format (digital signal) that can be processed by a computer with a high-speed sampling period (for example, 1 msec) and takes in the data in time series. The data of the signal to be analyzed among the captured time-series digital signals is sent to the frequency analysis unit 103.

The frequency analysis unit 103 executes frequency analysis on the time series data based on the above-described Fourier analysis algorithm. When performing a Fourier analysis, the sampling period of the signal used for analysis and the number of data used for analysis have important meanings as described above. That is, the sampling period determines the frequency band (range) to be analyzed, and the number of data used for analysis determines the resolution of analysis.

The frequency analysis unit 103 analyzes these analysis parameters using the data designated by the analysis parameter designation unit 108. For example, when the analysis parameter instructing unit 108 is set to use data of 100 msec sampling (10 Hz) and analysis is performed using 256 pieces of data, the frequency analyzing unit 103 outputs 0 to 5 Hz.
The frequency analysis result obtained by dividing the band of 256 into 256 is obtained.

In this case, data of 100 msec sampling is collected by the data input unit 102 for 1 mse.
The c-sampling data can be obtained by thinning out every 100 data.

It can be said that the analysis under such conditions is suitable for catching the change in a relatively low frequency band. For example, nuclear thermo-hydraulic vibration phenomena in the core of a nuclear power plant (ie core stability / channel stability)
The core neutron flux signal (average power range monitor)
The peak near 0.5 Hz of the power spectrum of APRM) is monitored, and the vibration of the in-core instrumentation tube is monitored by monitoring the 3 Hz component of the power spectrum of the core neutron flux signal (local power range monitor LPRM). Analysis of such parameter conditions is suitable for monitoring such low-frequency fluctuations (noise).

This parameter must be set in consideration of the processing speed and load of the computer, the time required for analysis, etc. That is, the processing load increases when processing data with a fast sampling period, and the larger the number of data used for analysis, the larger the amount of calculation and the higher the processing load. An appropriate parameter value is set based on such a calculation load problem and the frequency band of interest.

The result of the period analysis performed by the frequency analysis unit 103 is a two-dimensional pattern of frequency and power spectral density as shown in FIG.

The frequency analysis result thus obtained is sent to the comparison processing unit 104. In the comparison processing unit 104,
The sent data is compared with the data stored in the normal frequency characteristic database 105. The normal-time frequency characteristic database 105 is a database that stores process data of a normally collected plant. The comparison processing unit 104 reads out the normal frequency characteristic data of the same type as the target signal from the normal frequency characteristic database 105, and performs the following calculation for comparison.

(1) The difference between the normal pattern and the pattern at the time of diagnosis is taken for each frequency, and the values are added over the entire analysis frequency band to calculate the sum.

(2) For each frequency, the square of the difference between the normal pattern and the pattern at the time of diagnosis is taken, and the values are added together over the entire analysis frequency band to calculate the sum.

Next, the result of the comparison process is sent to the determination unit 106. The determination unit 106 compares and determines whether or not the value of the result of the comparison process that has been sent is within a certain set range. If the value is within the set range, it is determined to be normal, and if the value deviates from the range, it is determined to be abnormal. .

Therefore, the result judged by the judging unit 106 is transmitted to the judgment result notifying unit 107, and the result is notified to the outside. As a form of the notification, a method of being presented as a message on a CRT display device, a method of automatically starting another device, or the like can be considered.

On the other hand, the result determined by the determination unit 106 is also transmitted to the analysis parameter instruction unit 108. In the analysis parameter designating unit 108, when it is determined from the results of the analysis so far that some abnormality has occurred,
Adjust the analysis conditions to make the analysis more detailed. As a result, based on the result of the condition resetting in the analysis parameter instruction unit 108, the frequency analysis unit 103 performs analysis under the new condition and makes the same determination.

For example, when the number of data used for the analysis is changed to 512 or 1024 while the data having the same sampling period is used for the analysis carried out with 100 msec sampling and 256 data in the above example, the analysis is performed. Although the target frequency band remains 0 to 5 Hz, the resolution for dividing the range is high, and more detailed analysis is possible.

The sampling cycle of the target data is set to 1
0msec sampling (decimated data every 10)
When the conditions are set again to perform analysis with 1 msec sampling, the frequency bands to be analyzed are 0 to 50 Hz and 0 to 500 Hz, respectively, and the range of interest as the analysis target can be widened.

Further, the result determined by the determination unit 106 is transmitted to the data collection instruction unit 109. The data collection instruction unit 109 determines whether to save the time-series data of the process signal based on the result determined by the determination unit 106, and when saving, instructs the data storage processing unit 110 to that effect. . In the normal processing, when it is determined that an abnormality has occurred, an instruction is given to save the data at that time.

The data storage processing unit 110 collects the time-series data of the process input by the data input unit 102 based on the instruction from the data collection instruction unit 109, and saves it in the plant database 111. Here, as the data to be stored, in order to analyze the history before and after the abnormality, the data after a certain period of time is collected from the data before a certain period of time from the time when the abnormality is determined and stored.

The data stored in the plant database 111 has an important meaning as a time series representing the state of the plant when an abnormality occurs in the plant, and can be used for further detailed analysis of the content of the abnormality that has occurred in the plant. Is
Processing is performed on this data to perform analysis.

The time-series frequency analysis unit 112 obtains the time-series frequency characteristics of the data stored in the plant database 111 (how the frequency characteristics change with the passage of time). That is, the time series frequency analysis unit 11
In 2, the repetitive frequency analysis is performed on the time-series data of the plant process stored in the plant database 111 based on the parameters set by the primary analysis parameter setting unit 115. The parameters set by the primary analysis parameter setting unit 115 include the sampling period of data used for analysis, the number of data, and the interval at which the analysis is performed (how many points are advanced from the start point of the previous analysis when performing the next analysis). Whether to perform analysis from) and the number of times of analysis. These are set according to the characteristics of the signal to be analyzed.

The time-series frequency analysis unit 112 executes analysis based on these conditions, and the patterns of the frequency analysis results as shown in FIG. 5 or 6 are arranged in time series.
Time-series data of frequency analysis result data is created as shown in.

The frequency analysis result data shown in FIG. 5 will be described below.
(C) shows the measurement position, the solid line shows the data at the normal time, and the dotted line shows the actual data. Further, FIG. 6 also shows frequency analysis result data similar to FIG.
7 (C) indicates the measurement position, x indicates the data at the time of actual analysis, and · indicates the data at the normal time. Also,
FIG. 7 is a three-dimensional representation of time elements, frequencies, and power spectral densities, in which the frequency analysis result data shown in FIG. 5 or 6 is arranged in time series.

The analysis result obtained by the time-series frequency analysis unit 112 is further transmitted to the detailed analysis phase. In the next phase, the time series change of the characteristic frequency of the frequency analysis result is analyzed. Time series frequency analysis unit 112
The output from is the next analysis result time series data extraction unit 113.
Conveyed to. The analysis result time series data extraction unit 113 pays attention to the characteristic frequency set by the secondary analysis parameter setting unit 116, and specifies from the time series frequency analysis result shown in FIG. 7 obtained by the time series frequency analysis unit 112. The read frequency values are sequentially read and extracted. The data extracted in this way represents a time-series change in the characteristic frequency power.

The time-series data of the characteristic frequency thus obtained is sent to the next secondary frequency analysis unit 114.
The secondary frequency analysis unit 114 executes frequency analysis on the time-series data based on the parameters set by the secondary analysis parameter setting unit 116 described above. The analysis parameter used in this portion is the number of data used for frequency analysis. The analysis result obtained by the secondary frequency analysis unit 114 represents at what frequency the characteristic frequency changes, and is obtained as a two-dimensional pattern similar to that shown in FIG. 5 or 6.

The result of the frequency analysis executed by the secondary frequency analysis unit 114 is transmitted to the secondary determination unit 117, where it is determined whether it is normal or abnormal. Normal / abnormal judgment is
Various things can be considered. For example, determine whether the frequency showing the highest value is within the defined range,
There is a method of determining whether or not a value at a given frequency is within a given range. Further, as described above, a method of storing the analysis result in the normal state and making a determination by comparing with it may be considered.

The determination result of the secondary determination unit 117 is transmitted to the secondary determination result notification unit 118, and is notified to the outside from there. As the form of notification, the determination result notification unit 10 described above is used.
As in the case of 7, a method of presenting as a message on a CRT display device, a method of automatically starting another device, or the like can be considered. Further, the secondary determination result notifying unit 118 includes the analysis result time series data extracting unit 113 and the secondary frequency analyzing unit 1.
The data from 14 is also transmitted, and each result can be notified to the outside.

As described above, according to the third embodiment, in the abnormality diagnosis of the nuclear power plant, the diagnosis can be performed by the analysis range and resolution according to the state of the plant, and the details of the abnormality can be grasped. It will be possible. In addition, the data at the time of occurrence of an abnormality is saved as a database, and the situation at the time of occurrence of an abnormality can be reproduced after the fact, and more detailed analysis can be performed. Furthermore, by performing a secondary frequency analysis on the stored data, it is possible to perform an analysis that captures a time-series change in the characteristic frequency, and a more detailed diagnosis is possible.

In the third embodiment, an example applied to a nuclear power plant has been described, but a similar diagnostic system can be applied to a thermal power plant, a chemical plant, and a steel plant. In that case, the analysis parameter instruction unit,
The values of the parameters set by the primary parameter designating unit and the secondary parameter designating unit and the data stored in the normal frequency characteristic database are characteristic of each plant.

Further, the judgment processing for judging the presence or absence of an abnormality in the analysis result is not limited to the one shown in the above-mentioned embodiment, and a method of comparing waveform patterns using an image processing method or a simple method is used. There is also a method of comparing the number of peaks and the value. That is, the algorithm is not particularly limited as long as it is a method of comparing two patterns.

[0125]

As described above, according to the first aspect of the present invention, the setting of the analysis parameter is changed according to the determination result data of the online frequency analysis. The frequency band or the number of data can be changed, more detailed frequency analysis can be performed, and online diagnosis can be executed according to the situation of the plant.

According to the invention of claim 2, since the plant data is stored in the plant database by the online determination result data, further detailed analysis can be performed by using the plant data stored in the plant database. Can perform plant diagnosis.

According to the third aspect of the present invention, frequency analysis is repeatedly performed on the plant data stored in the plant database using the primary analysis parameters, and the obtained frequency analysis result data are arranged in time series to obtain time series frequencies. Time-series data of the characteristic frequency is extracted from the analysis result data. This allows frequency analysis to be narrowed down from time-series data of characteristic frequencies.

According to the invention of claim 4, the secondary frequency analysis is executed from the time-series data of the characteristic frequency by using the time-series data of the characteristic frequency by the secondary analysis parameter, and the change state of the time-series data of the characteristic frequency is executed. Can be analyzed,
More detailed diagnosis is possible.

According to the fifth aspect of the present invention, the frequency analysis result data obtained by arranging the frequency analysis result data obtained by repeating the frequency analysis using the plant data in a relatively wide frequency band as the analysis condition is arranged in time series. The time-series data is extracted, and the time-series data of the characteristic frequency is extracted by extracting the value of the characteristic frequency from the time-series data of the frequency analysis result data. Thereby, more detailed diagnosis can be performed from the time-series data of the characteristic frequency.

According to the sixth aspect of the present invention, frequency analysis can be performed by narrowing down to the characteristic frequency band, and detailed diagnosis can be performed.

According to the invention of claim 7, the sampling period and the number of data can be set and changed as analysis conditions depending on the situation of the plant.

According to the eighth aspect of the present invention, the sampling period as the analysis parameter and the number of data are set according to the characteristics of the signal to be analyzed, and the iterative frequency analysis is performed. To be

According to the ninth aspect of the present invention, detailed plant diagnosis can be performed because the frequency band as the analysis parameter and the number of data are set according to the characteristics of the signal to be analyzed.

[Brief description of drawings]

FIG. 1 is a configuration diagram of a plant diagnostic device showing a first embodiment of the present invention.

FIG. 2 is an explanatory diagram showing an example of frequency analysis data obtained by a frequency analysis unit included in the plant diagnostic device of FIG.

FIG. 3 is a configuration diagram of a plant diagnostic device showing a second embodiment of the present invention.

FIG. 4 is a configuration diagram of a plant diagnostic device showing a third embodiment of the present invention.

5 is an explanatory diagram showing a first example of frequency analysis data obtained by the plant diagnostic device of FIG. 4. FIG.

6 is an explanatory diagram showing a second example of frequency analysis data obtained by the plant diagnostic device of FIG.

7 is an explanatory diagram showing time-series data of frequency analysis result data obtained by a primary analysis unit provided in the plant diagnostic device of FIG.

FIG. 8 is a configuration diagram of a plant diagnostic device showing a conventional example.

[Explanation of symbols]

 101 data transmission line 102 data input unit 103 frequency analysis unit 104 comparison processing unit 105 normal frequency characteristic database 106 determination unit 107 determination result notification unit 108 analysis parameter instruction unit 109 data collection instruction unit 110 data storage processing unit 111 plant database 112 hours Sequence frequency analysis unit 113 Analysis result time series data extraction unit 114 Secondary frequency analysis unit 115 Primary analysis parameter setting unit 116 Secondary analysis parameter setting unit 117 Secondary determination unit 118 Secondary determination result notification unit 120 Primary analysis unit 121 Secondary Analysis department

Front page continuation (72) Inventor Masahiro Horiguchi 1st in Toshiba Fuchu factory, Fuchu, Tokyo (72) Inventor Makoto Fujii 1st in Toshiba Fuchu, Tokyo Fuchu factory

Claims (9)

[Claims] 1. A data input unit for fetching plant data, and an on-line frequency analysis is performed by using the data fetched by this data input unit to perform an online frequency analysis with an analysis parameter consisting of a predetermined sampling period and a predetermined number of data. A frequency analysis unit that outputs frequency analysis data,
Normal frequency characteristic database that saves the frequency analysis result when the plant is normal as normal frequency analysis data, and compares the online frequency analysis data and the normal frequency analysis data to determine the state of the plant In a plant diagnostic device having a determination unit that outputs the result data, and a determination result notification unit that notifies the determination result data by this determination unit to the outside, in accordance with the determination result data taken in the determination result data from the determination unit A plant diagnostic device comprising an analysis parameter designating unit for changing the setting of an analysis parameter analyzed by a frequency analysis unit. 2. A data collection instruction unit for taking in the determination result data from the determination unit and determining whether or not to save the plant data input by the data input unit based on the determination result data, and the data collection instruction. 2. The plant diagnostic apparatus according to claim 1, further comprising a data storage processing unit that fetches plant data from the data input unit and stores the plant data in the plant database when the unit determines to store the plant data. 3. A data input unit for taking in plant data, and an on-line frequency analysis is performed by using the data taken in by the data input unit to conduct an online frequency analysis with an analysis parameter consisting of a predetermined sampling period and a predetermined number of data. A frequency analysis unit that outputs frequency analysis data,
Normal-time frequency characteristic database that saves frequency analysis results when the plant is normal as normal-time frequency analysis data, and determines the state of the plant by comparing the online frequency analysis data and the normal-time frequency analysis data In a plant diagnostic device having a determination unit that outputs result data, and a determination result notification unit that notifies the determination result data by this determination unit to the outside, the determination frequency is taken from the determination unit, and the frequency is determined according to the determination result data. An analysis parameter instructing unit that changes the setting of the analysis parameters analyzed by the analysis unit, and the determination result data by the determination unit is taken in, and whether or not to save the plant data input by the data input unit based on the determination result data is determined. The data collection instructing part to judge and the plant data by this data collection instructing part When it is determined that the data is to be delivered, a data storage processing unit that captures plant data from the data input unit and stores the plant data in the plant database, and frequency analysis is repeatedly performed on the plant data stored in the plant database using primary analysis parameters. And a primary analysis unit that extracts time-series data of a characteristic frequency from the time-series frequency analysis result data obtained in this way. 4. The secondary frequency analysis result data obtained by performing detailed analysis with secondary analysis parameters based on the time-series data of the characteristic frequency obtained by the primary analysis unit and determining the determination result. The plant diagnostic apparatus according to claim 3, further comprising a secondary analysis unit that notifies the outside. 5. The primary analysis unit sets a primary analysis parameter setting unit that sets a primary analysis parameter having a wide frequency band as an analysis condition, and a plant of the plant database according to the primary analysis parameter set by the primary analysis parameter setting unit. A time-series frequency analysis unit that outputs a time-series frequency analysis result data by performing repetitive frequency analysis using the data, and a characteristic frequency of the characteristic frequency from the time-series frequency analysis result data obtained by the time-series frequency analysis unit. The plant diagnostic device according to claim 3, further comprising an analysis result time-series data extraction unit that extracts time-series data. 6. The secondary analysis unit is configured by a secondary analysis parameter setting unit that sets a secondary analysis parameter in which a frequency band is narrowed down to a characteristic frequency band as an analysis condition, and the secondary analysis parameter setting unit. A secondary frequency analysis unit that executes a frequency analysis based on the secondary analysis parameters, a secondary determination unit that determines the state of the plant based on the frequency analysis data obtained by this secondary frequency analysis unit, and a determination performed by this secondary determination unit The plant diagnosis device according to claim 4, further comprising a secondary determination result notification unit that notifies the result to the outside. 7. The analysis parameter set and changed by the analysis parameter instruction unit is either one or both of a sampling period and the number of data, according to any one of claims 1 to 6. That plant diagnostic device. 8. The plant diagnostic device according to claim 5, wherein the analysis parameters set by the primary analysis parameter setting unit are a sampling period and the number of data. 9. The plant diagnostic device according to claim 6, wherein the secondary analysis parameters set by the secondary analysis parameter setting unit are a frequency band and the number of data.