JP2850917B2 - Diagnosis method for rotating machinery - Google Patents

Description

Description: TECHNICAL FIELD The present invention relates to a method for diagnosing an abnormality of a rotating machine such as a power generating turbine or a generator.

(Prior art) A thermal power plant is equipped with a large number of rotating machines, including a main turbine / generator in which a rotor with a heavy weight of several hundred tons rotates at a high speed, and is adapted to a wide range of temperatures and pressure levels. Numerous heat exchangers, and various hydraulic equipment and control equipment are installed.

In such a rotating machine, especially in a high-speed rotating machine, many of the abnormalities that occur during operation often appear as changes in vibration phenomena, and there is a danger that rapid progress will develop into serious troubles However, it is equipped with a detailed operation monitoring device and a protection / control device in the event of an abnormality.

Especially in recent years, frequent start-ups and
There is a need for diversified operations such as stoppages and sudden load fluctuations, and it is increasingly demanded that signs of equipment abnormality be detected at an early stage, the cause be analyzed, and conditions such as operation restrictions can be quickly restored. It has become

For this reason, various abnormality diagnosis apparatuses and equipment diagnosis apparatuses for rotating machines have been proposed. For example, Japanese Patent Application Laid-Open No. 61-128128, "Abnormal Diagnosis Apparatus for Rotating Body", etc., has been proposed with regard to shaft vibration diagnosis of rotating machines.

FIG. 7 shows an example in which a conventional vibration diagnostic device is applied to a main turbine / generator unit of a rotary machine for thermal power generation.

In the figure, the height of the main turbine / generator unit 1
Vibration detectors 6a to 6n that detect shaft vibration are attached to bearings 5a to 5n that support the rotors of the medium-pressure turbine 2, the low-pressure turbines 3a and 3b, and the generator 4, respectively. In the unit 1, a tachometer 7 for detecting the number of rotations of the rotor,
A phase angle reference pulse transmitter 8 for transmitting a pulse serving as a reference for detecting a vibration phase angle at 5a to 5n, and detecting an operating state of the rotating machine such as a generator output (load), a steam temperature, a bearing temperature, and the like. Various sensor groups 9 are provided.

The signals from the tachometer 7 and the various sensor groups 9 are sent to a control computer 10 for monitoring and controlling the operation of the rotating machine. The signals from the vibration detectors 6a to 6n and the phase angle reference pulse transmitter 8 are Control computer via monitoring device 11
Sent to 10 to assist operator control.

That is, the vibration monitoring device 11 determines the vibration state of each of the bearing portions 5a to 5n, and transmits an alarm signal or a turbine trip signal to the control computer 10 when it determines that the signal level is equal to or higher than a specified signal level. The vibration signals from the vibration detectors 6a to 6n and the signals from the various sensor groups 9 are also sent to the analog recorder 12, and are continuously recorded.

The rotating machine abnormality diagnosis device 13 includes an abnormality detection processing unit 14, a state determination processing unit 15, a diagnosis processing unit 16, a display processing unit 17,
The signal from the vibration monitoring device 11 is guided to the abnormality detection processing unit 14.

The abnormality detection processing unit 14 detects the sign of the abnormal phenomenon as the amplitude value of the vibration,
If it is detected from a tendency such as an amplitude increase rate, a frequency component, or a phase change, and is determined to be an abnormal sign, the detection information is transmitted to the diagnosis processing unit 16 to start diagnosis of the cause.

On the other hand, operating state information other than the vibration information, for example, information such as the number of revolutions of the main turbine / generator and the load factor is stored in the control computer 10.
Is input to the state determination processing unit 15 via the. The output of the state determination processing unit 15 is sent to the abnormality detection processing unit 14 and used as information at the time of abnormality detection, and is sent to the diagnosis processing unit 16 and used as rotary machine information at the time of cause diagnosis. It is sent to the display processing unit 17 to display the monitoring status and the diagnosis result.

The diagnostic processing unit 16 has a built-in basic matrix.
There are many types and contents of this basic matrix. For example, FIG. 8 illustrates a part of a basic matrix showing a causal relationship between a vibration cause and a phenomenon in a rotating body, and FIG. 9 illustrates a basic matrix relating to deterioration diagnosis.

The diagnostic processing unit 16 estimates the cause of the abnormality based on the abnormality detection information and the rotating machine information using these basic matrices.

The output of the diagnosis processing unit 16 is sent to the display processing unit 17, and is used as direct support and guide information for the operator and the operator. Data that needs to be stored is stored in the data storage processing unit 18.
And stored as history data. These history data include a reference value indicating a normal state and a record at the time of a past abnormality, and are input to the abnormality detection processing unit 14 and the diagnosis processing unit 16 for use as necessary.

(Problems to be Solved by the Invention) Real-time performance is required to perform diagnosis using on-line signals and data of the rotating machine, such as diagnosis of shaft vibration of the rotating machine and performance degradation of the rotating machine. Regarding the shaft vibration phenomenon that may cause the abnormal phenomenon to progress, it is necessary to make a determination according to the operating state and the abnormal state of the rotating machine.

However, in the conventional abnormality diagnosis device, even if the abnormality level is recognized at the stage of the monitoring function by the abnormality detection processing unit 14, at the stage of the basic matrix of the cause diagnosis, the determination of the symptom stage at which the cause of the abnormal symptom is missing is performed. It is a guide.

Therefore, even when the abnormal sign is at a low level or when the phenomenon has progressed, the diagnostic processing standard does not change, and for example, a serious abnormal sign may be erroneously recognized as a minor vibration cause.

In order to avoid such danger, a method using a plurality of basic matrices is conceivable.However, processing such as selection of criteria is complicated, and the real-time performance of diagnosis may be impaired. It is necessary to greatly increase the processing power of the computer.

(Object of the Invention) The present invention has been made in order to solve the above-mentioned drawbacks in the prior art. By using a diagnosis algorithm suitable for the time series change of abnormal phenomena, real-time characteristics and manpower for the operator can be attained. It is an object of the present invention to provide a method for diagnosing an abnormality in a rotating machine having excellent mechanical properties.

(Means for Solving the Problems) The abnormality diagnosis method for a rotating machine according to the present invention includes an operation information table indicating an operation state of the rotating machine based on various data obtained from the rotating machine, and an abnormality for each abnormality detection item. And a basic matrix that reflects the contents of the driving information table and the abnormality detection information table in an abnormality symptom matrix representing the relationship between the abnormality sign and its cause. And
Then, based on a magnification matrix indicating the strength of association between the abnormality detection item and the abnormality level and the abnormality detection information table, create a diagnosis matrix in which each element of the basic matrix is weighted according to the abnormality level, Diagnosing the cause of the abnormality occurring in the rotating machine based on the diagnosis matrix.

(Operation) According to the rotary machine abnormality diagnosis method of the present invention, an operation information table indicating an operation state of the rotary machine, an abnormality detection information table indicating an abnormality level for each abnormality detection item, an abnormality detection item and an abnormality By using the magnification matrix indicating the strength of connection with the level, the weight of each element of the diagnosis matrix for performing the abnormality diagnosis can be dynamically changed according to the level of the abnormality.

For this reason, when driving conditions or abnormal levels change,
Diagnosis according to the situation can be performed.

Therefore, according to the present invention, it is possible to make a diagnosis according to the progress of the abnormality, to provide timely information to the rotating machine operator, and to greatly contribute to the safe operation and improvement of the reliability of the rotating machine. it can.

(Example) Hereinafter, an example in which the present invention is applied to vibration diagnosis of a rotating machine will be described.

In the following description, the same portions as those in FIG. 7 are denoted by the same reference numerals, and the description thereof will be omitted unless necessary.

In FIG. 1, an abnormality diagnosis device 20 includes an abnormality detection processing unit 14.
, A state determination processing unit 21, a diagnosis matrix processing unit 22
, A diagnosis processing unit 23, a display processing unit 17, and a data storage processing unit 18.

The abnormality detection processing unit 14 analyzes a vibration signal (analog signal) sent via the vibration monitoring device 11 and detects a sign of an abnormal phenomenon.

The state determination processing unit 21 analyzes rotating machine information (digital signal) from the control computer 10 and the like, and outputs the result to the abnormality detection processing unit 14, the display processing unit 17, and the diagnostic matrix processing unit 22.
Output to

The diagnosis matrix processing unit 22 creates a diagnosis matrix considering the abnormality level from the abnormality detection information and the rotating machine information.

Further, the diagnostic processing unit 23 determines the cause of vibration based on the diagnostic matrix, the output of which is sent to the display processing unit 17,
It is used as direct support and guide information for operators and operators. The data that needs to be stored is guided to the data storage processing unit 18 and stored as history data.

FIG. 2 shows the flow of a signal introduced into the abnormality diagnosis device. The rotating machine data is led to a state determination processing unit 21.
Area determination 21a is performed for each rotation speed and operating load, and threshold setting 21b for abnormality determination is performed. The rotating machine data necessary for the diagnosis is also sent to the diagnosis matrix processing unit 22.

The abnormality detection processing unit 14 determines the vibration data. In making this determination, not only the amplitude and increase rate of the vibration data,
Processing such as frequency analysis and vector analysis is executed as the abnormality detection 14a, and detection 14b of the presence or absence of an abnormality is performed based on the previously set threshold value. In this case, the past history data stored in the data storage processing unit 18 is used for comparison.

In the case of No in which no sign of abnormality is detected by the above processing, a loop returning to the operation region determination 21a is repeated, and data is analyzed periodically. On the other hand, in the case of YES in which a sign of abnormality is detected, the data is sent to the next diagnosis matrix processing unit 22.

The diagnosis matrix processing unit 22 creates a detection information table 22a based on the transmitted data. The detection information table includes an operation information table 22b shown in FIG. 3A in which each information of an operation area, an operation mode, and an occurrence position of an abnormality is combined, and FIG. 3B showing an abnormality level for each abnormality detection item. The abnormality detection information table 22c shown in FIG.

Next, the diagnosis matrix processing unit 22 creates a basic matrix 22e from the operation information table 22b, the abnormality detection information table 22c, and the abnormality sign matrix 22d as shown in FIG.

That is, the abnormal symptom matrix 22d is shown in FIGS.
As shown in (1), the relationship between the cause of the abnormality and the sign of the abnormality, such as the operating region in which the abnormality occurs and the condition for detecting the abnormality, is predetermined in accordance with the characteristics of the machine.

A basic matrix 22e is obtained by extracting a portion where the data described in the operation information table 22b of FIG. 3A and the abnormality detection information table 22c of FIG. 3B coincide with the abnormality symptom matrix 22d.

In the basic matrix 22e shown in FIG. 5, the edited matrices are indicated by symbols such as △, 等, and the like, but these are converted into points as predetermined to generate basic points.

Subsequently, the diagnosis matrix processing unit 22 determines a magnification to be multiplied by a portion where the level of the abnormality described in the abnormality detection information table 22c matches the data described in the magnification matrix illustrated in FIG. Then, a score obtained by multiplying each row of the detection condition of the basic matrix 22e by the magnification is created in the diagnostic matrix 22g.

Thereafter, the diagnosis processing unit 23 diagnoses the cause of the abnormality based on the diagnosis matrix 22g created as described above. In this case, for example, the score of each element of the diagnosis matrix 22g is added, and the presence or absence of the possibility and the height are determined from the ratio of the score for each cause to the total score.

In the above description, the magnification matrix 22f works particularly effectively when trying to determine an item having a subordinate relationship between the determination items of the abnormality cause. That is, for example, when the causal relationship between the rubbing phenomenon and the vibration amplitude value is expressed using the vibration amplitude value and the vector change amount, a strong causal relationship is given to both the amplitude value change and the vector change amount in the basic matrix. This condition should naturally appear in the vector change amount when the amplitude value is large, and the magnification is determined so that the abnormality detection of the vector change amount becomes a large ratio depending on the abnormal level of the amplitude value. Conversely, when the amplitude value is large, if there is no abnormality detection in the vector change amount, the possibility of the rubbing phenomenon can be more strongly reduced, and the possibility of another cause item can be increased.

As described above, according to the present embodiment, it is possible to obtain a diagnostic matrix having a weight corresponding to the level of abnormality based on the abnormality detection information table and the magnification matrix. Although it was difficult to distinguish from other cause items, when the level of abnormality became high, the information was obtained and the differentiation from other causes was clarified. Obtainable.

Note that the contents of the “weight” in the abnormal symptom matrix and the “coefficient” of the magnification matrix match in the sense of changing the score of the matrix. However, different terms were used because the evaluation score was changed by multiplying the “weight” of the abnormal symptom matrix by the “coefficient” of a magnification matrix that changes depending on the level of abnormality.

(Effects of the Invention) As described above, according to the method for diagnosing abnormality of a rotating machine of the present invention, a diagnosis matrix for diagnosing abnormality based on not only the abnormality detection condition but also the abnormality level, the operating condition, and the conditions among the causes. Can be dynamically changed.

For this reason, when the driving situation and abnormal level change,
Diagnosis according to the situation can be easily performed.

At this time, the basic matrix of the causal relationship that is the basis can be summarized into a relatively simple small matrix, and not only the judgment speed of the computer but also the secondary knowledge such as easy construction of the knowledge base for diagnosis. It has the following effects:

According to the present invention, an abnormality can be detected early on-line and in real time, and a diagnosis can be made in accordance with the progress of the abnormality. By providing timely information to a rotating machine operator, This can contribute to the safe operation and improved reliability of the rotating machine.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an example in which the present invention is applied to vibration diagnosis of a rotating machine, FIG. 2 is a flowchart for explaining the operation of an embodiment of the present invention, and FIGS. FIG. 4 is an explanatory diagram exemplifying a driving information table, an abnormality detection information table, and a magnification matrix in the example, FIG. 4 is an explanatory diagram exemplifying an abnormality sign matrix in an embodiment of the present invention, and FIG. FIG. 6 is an explanatory diagram illustrating the creation of a basic matrix, FIG. 6 is an explanatory diagram illustrating a diagnostic matrix in an embodiment of the present invention, FIG. 7 is a block diagram illustrating a conventional plant abnormality diagnostic device,
FIG. 8 is an explanatory view exemplifying a part of an abnormal symptom matrix showing a causal relationship between a vibration cause and a phenomenon in a rotating body, and FIG.
The figure is an explanatory view exemplifying an abnormal symptom matrix related to performance degradation diagnosis. 1 ... Main turbine / generator unit, 2 ... High / medium pressure turbine, 3a, 3b ... Low pressure turbine, 4 ... Generator, 5a-5
n: Bearing part, 6a to 6n: Vibration detector, 7: Tachometer,
8 ... Phase angle reference pulse transmitter, 9 ... Sensor group, 10 ... Control computer, 11 ... Vibration monitoring device, 12 ...
Analog recorder, 13… Abnormal diagnosis device, 14… Abnormal detection processing unit, 15… Status judgment processing unit, 16… Diagnosis processing unit,
17 display processing unit, 18 data storage processing unit, 20 abnormality diagnosis device, 21 state determination processing unit, 22 diagnostic matrix processing unit, 23 diagnostic processing unit.

Claims (1)

(57) [Claims] 1. An operation information table indicating an operation state of a rotating machine and an abnormality detection information table indicating an abnormality level for each abnormality detection item are created based on various data obtained from the rotating machine. A basic matrix that reflects the contents of the driving information table and the abnormality detection information table in an abnormality symptom matrix representing the relationship between the symptoms of the abnormality and the cause thereof, and then creates a strong link between the abnormality detection item and the abnormality level. A diagnostic matrix is created by weighting each element of the basic matrix in accordance with the abnormality level based on the magnification matrix representing the error and the abnormality detection information table. A method for diagnosing an abnormality of a rotating machine, characterized by diagnosing the cause of the rotation.