JP6405486B1 - Wind power generation facility abnormality sign diagnosis system, management device, and abnormality sign diagnosis method - Google Patents

Abstract

An abnormality sign diagnosis system for a wind turbine generator capable of accurately predicting a replacement timing of a spindle bearing. An abnormality sign diagnosis system for a wind power generation facility includes a wind power generation facility and a management device 100. The management device 100 determines the main shaft bearing at that time according to the state of grease of the main shaft bearing of the wind power generation facility. The current diagnosis mode unit 23 for diagnosing abnormal signs of the wind power generation, the vibration value of the main shaft bearing is acquired and used as the initial vibration value, and the main shaft of the wind power generation facility estimated from the time when a predetermined time has elapsed from the start of operation of the wind power generation facility A predictive diagnosis mode unit for diagnosing a future sign of abnormality of the main shaft bearing based on the accumulated rotational speed and the initial vibration value at the time, and predicting the replacement time of the main shaft bearing; [Selection] Figure 2

Description

  The present invention relates to an abnormality sign diagnosis system, a management apparatus, and an abnormality sign diagnosis method for wind power generation facilities.

  As a system for diagnosing an abnormality in a device provided in a wind turbine generator, a state monitoring system disclosed in Patent Document 1 includes a monitor device including a sensor provided in the device, and a threshold used by the monitor device to diagnose the device abnormality. And a monitoring-side control device that diagnoses an abnormality of a device based on a threshold value, and a monitoring terminal that monitors the state of the device.

International Publication No. 2013/133002

  With the increase in wind power generation facilities that have been operating for more than 10 years, there may be a case where an operation stoppage occurs due to an abnormality in a main shaft bearing that is a main component. Since this restoration takes a considerable amount of time, a prediction system that predicts when the main shaft bearings cannot be operated in advance and allows the main shaft bearings to be systematically replaced is required.

  Conventionally, in order to diagnose the deterioration state of a main shaft bearing of a wind power generation facility, as described in Patent Document 1, it is diagnosed whether or not there is an abnormality by comparing the frequency (diagnostic parameter) of the main bearing with a predetermined threshold value. doing. However, there is only a very conceptual description of whether or not the spindle bearing failure is predicted to approach the threshold value, and a planned spindle bearing replacement operation cannot be realized.

  The present invention is an invention for solving the above-described problems, and provides an abnormality sign diagnosis system, a management apparatus, and an abnormality sign diagnosis method for a wind turbine generator capable of accurately predicting a replacement timing of a spindle bearing. With the goal.

In order to achieve the above object, an abnormality sign diagnosis system for a wind power generation facility according to the present invention includes a wind power generation facility and a management device, and the management device at that time depends on a state of grease of a spindle bearing of the wind power generation facility. The current diagnosis mode unit for diagnosing an abnormal sign of the main shaft bearing at the time, and acquiring the vibration value of the main shaft bearing as an initial vibration value, from the time when a predetermined time has elapsed from the start of operation of the initial vibration value and wind power generation equipment diagnosing an abnormality sign of the main shaft bearing in the future based on the cumulative number of rotations of the main shaft of the wind power installation of estimating, possess a prediction diagnostic mode unit that predicts time for replacement of the main shaft bearing, a management apparatus, the current diagnostic mode In the unit, a predetermined image processing is performed on the image information of the grease in the spindle bearing acquired by the imaging device to calculate the degree of contamination of the foreign matter in the grease, When an abnormality sign of the spindle bearing is diagnosed based on the first abnormality degree calculated according to the mixing degree, and there is an abnormality sign of the spindle bearing when the first abnormality degree of the current diagnosis mode unit reaches the first reference abnormality degree Diagnose and shift to a predictive diagnosis mode unit . Other aspects of the present invention will be described in the embodiments described later.

  According to the present invention, it is possible to accurately predict the replacement time of the spindle bearing.

It is a wind power generation equipment which concerns on embodiment, (a) is a windmill external view, (b) is an enlarged view inside a nacelle. It is a figure which shows the structure of a management apparatus. It is a figure which shows the structure around a spindle bearing and a greasing port. It is a flowchart which shows the process of abnormality diagnosis / precursor diagnosis of a management apparatus. It is a figure which shows an example of the image of abnormality diagnosis of the present diagnosis mode part. It is a figure which shows the prediction method of the replacement | exchange time of the spindle bearing in a prediction diagnosis mode part. It is a figure which shows an example of each month reference | standard rotation speed, (a) is an example at the time of operating throughout the year, (b) is an example in case there exists a non-operation period. It is a figure which shows an example of the prediction diagnostic result displayed on the display part of a management apparatus.

DESCRIPTION OF EMBODIMENTS Embodiments for carrying out the present invention will be described in detail with reference to the drawings as appropriate.
FIG. 1 shows a wind turbine generator 10 (windmill) according to the embodiment, (a) is an external view of the windmill, and (b) is an enlarged view inside the nacelle 3. In the present embodiment, a direct drive wind power generator (direct drive wind power generator) will be described as an example. The wind power generation facility 10 includes a blade 2 mounted on the hub 1, a nacelle 3 (housing), and a tower 4 (support) that supports the nacelle 3. Inside the nacelle 3, the main shaft 11 on the fixed side that is hollow and supported by the support portion 19 of the tower 4, and the generator 5 is disposed on the outer periphery of the main shaft 11.

  The generator 5 includes a stator 6 and a generator rotor 7. The stator 6 is fixed to the stator casing 9. The generator rotor 7 includes a field magnet and a rotor plate 8 that supports the field magnet, and the rotor plate 8 is coupled to the main shaft 12 on the rotation side.

  The main shaft 12 is supported on the main shaft 11 by two main shaft bearings 13 and 14 (main bearings) and is connected to the hub 1. Further, the protrusion 16 of the rotor plate 8 is supported by the stator casing 9 by a third bearing 15.

  A service person who performs periodic inspection of the wind power generation facility 10 installs the sensor 50 in the vicinity of the inner rings of the spindle bearings 13 and 14. More specifically, a sensor 50 for measuring and monitoring the state (operation state signal) of the main shaft bearings 13 and 14 is installed in a hollow portion of the main shaft 11 and located radially inward of the main shaft bearings 13 and 14. . The sensor 50 is at least one of an acoustic sensor, a vibration sensor, and an AE sensor. Note that AE (Acoustic Emission) is a phenomenon in which elastic energy stored inside is released as sound waves (elastic waves, AE waves) when a material is deformed or destroyed. The AE wave mainly has a high frequency component in the ultrasonic region (several tens of kHz to several MHz).

  The management device 100 (see FIG. 2) acquires sensor data from the sensor 50 via the amplifier 51, the data collection device 52, and the network NW, and based on the acquired sensor data and operation data, the spindle bearing replacement time Predict. The management device 100 may be arranged in a management ridge on the ground.

  FIG. 2 is a diagram illustrating a configuration of the management apparatus 100. As illustrated in FIG. 2, the management device 100 includes a processing unit 20, a storage unit 30, an input unit 41, a display unit 42, an imaging unit 43 (imaging device), and a communication unit 44. The processing unit 20 includes a sensor data acquisition unit 21 that acquires a detection signal (sensor data) from the sensor 50, an operation data acquisition unit 22 that acquires operation data (rotor rotation speed, rotor rotation speed, wind speed, etc.), and wind power generation equipment. 10 spindle bearings 13, 14 according to the state of lubricating grease, a current diagnosis mode section 23 (first diagnosis mode section) for diagnosing an abnormal sign of the spindle bearing at that time, and spindle bearings 13, 14 based on sensor data To obtain the initial value of vibration, and based on the initial value of vibration and the cumulative number of rotations of the main shaft of the wind power generation equipment estimated from the time when the predetermined time has elapsed from the start of operation of the wind power generation equipment. A predictive diagnosis mode unit 24 for diagnosing abnormal signs and predicting the replacement timing of the spindle bearing.

  In the storage unit 30, the lubrication grease image 31, the lubrication grease determination result 32, the sensor data 33, the operation data 34, the monthly reference rotational speed 35 (see FIG. 7), and the main shaft bearings 13 and 14 are exchanged. The time prediction result 36 and the like are stored. The monthly reference rotational speed is the total value of the rotational speeds of the spindle bearings stored last month in the operation year (excluding the non-operating year) of the wind power generation facility 10 in the past predetermined period. Details will be described later with reference to FIG.

  As a result of observing the vibration values of the spindle bearings 13 and 14 of the plurality of wind power generation facilities 10, the inventors have found that it is not always possible to diagnose an abnormality only with the vibration values. In other words, it has been found that some wind power generation facilities 10 have individual differences, and some maintain a normal state even when the vibration value approaches a predetermined threshold value.

  In addition, the inventors have experienced that the current state of the spindle bearing is particularly important, and paid attention to the lubricating grease as an index thereof. In order to replace the spindle bearing systematically based on changes in the state of the spindle bearing, a method that compares the current vibration value of the spindle bearing with a predetermined threshold value is used to predict the replacement timing of the spindle bearing. It was judged that it was necessary to predict the future deterioration state from the past state change.

  To solve this, it was found that it was necessary to make a diagnosis based on changes in the state of the spindle bearing before making a diagnosis based on the vibration value. That is, it is necessary to first diagnose based on the foreign matter mixed state in the lubricating grease in the main shaft bearing. In the current diagnosis mode section 23 (primary diagnosis mode section), it is determined whether or not an abnormality has occurred in the spindle bearing based on the state of the lubricating grease. In the mode unit 24, a method of diagnosing based on the vibration value is adopted.

  FIG. 3 is a view showing the structure around the spindle bearing 14 and the greasing port 146. The main shaft bearing 14 includes an outer ring 141, a rolling element 142 (for example, a roller), a cage (not shown) for determining the position of the rolling element 142, and an inner ring 143. The main shaft 11 is fitted to the inner ring 143, and the main shaft 12 is fitted to the outer ring 141. The sensor 50 is installed on the inner surface of the main shaft 11 via a sensor jig. The sensor 50 detects a sound (shock wave) generated when a defect occurs in the raceway surface of the outer ring 141 or the inner ring 143 and the rolling element 142 contacts the defect as a signal. The main shaft bearing 13 has the same configuration as the main shaft bearing 14.

  In FIG. 3, the case where the main shaft 11 and the inner ring 143 are independent members has been described. However, the inner ring 143 of the main shaft bearing 14 may play the role of the main shaft. In that case, it is preferable to install the inner ring 143 (inner ring portion) so that the detection surface of the sensor 50 contacts the inner wall and faces the outer ring outer wall side of the spindle bearing 14.

  The main shaft bearing 14 has a structure in which lubricating grease can be supplied from a greasing port 146 attached to the bearing cover 145. The service person periodically collects the lubricating grease of the main shaft bearing 14 from the greasing port 146. The management device 100 manages the state of the collected lubricating grease.

  FIG. 4 is a flowchart showing the current diagnosis / predictive diagnosis processing of the management apparatus 100. This will be described with reference to FIG. The current diagnosis mode unit 23 acquires an image of the lubricating grease state by the imaging unit 43 (step S41), and performs binarized image processing by black and white (step S42). The current diagnosis mode unit 23 calculates the white ratio and determines the foreign substance contamination degree (ratio) (see step S43, FIG. 5). If the foreign substance contamination degree is less than the reference abnormality degree (step S43, less than the reference abnormality degree). ) Assuming that the spindle bearing is normal (step S45), the process returns to step S41. If the degree of contamination is greater than or equal to the reference abnormality degree (step S43, greater than or equal to the reference abnormality degree), the current diagnosis mode unit 23 diagnoses that there is a sign of abnormality in the spindle bearing, and shifts to the prediction diagnosis mode (step S44).

  The binarization is a process of converting an image into two gradations of white and black. A threshold value is determined in advance. If the pixel value is larger than the threshold value, white (1) is obtained. 0).

  The prediction diagnosis mode unit 24 acquires the vibration value at the time of diagnosis that there is an abnormality sign of the spindle bearing from the operation data 34, and sets the acquired value as the vibration initial value (step S46). The possible rotation speed up to the abnormality determination threshold for each model is predicted based on the data of the monthly reference rotation speed 35 (see step S47, FIG. 6 and FIG. 7), and the spindle bearing replacement time is predicted (step S48). ).

  FIG. 5 is a diagram illustrating an example of an abnormality diagnosis image in the current diagnosis mode unit, where (a) is an image indicating a healthy state and (b) is a diagram illustrating an abnormal state. In the case of the healthy state shown in FIG. 5A, when the lubrication grease state after imaging is binarized, it is almost black and less than the reference abnormality level, and is determined as “normal”.

  On the other hand, in the abnormal state of FIG. 5B, a large number of foreign matters are seen in the lubricated grease state after imaging, and appear as a plurality of white dots when binarized. At this time, if it is equal to or higher than the reference abnormality degree, it is determined as “abnormal”.

  In the current diagnosis mode unit 23 (first diagnosis mode unit), the management device 100 performs predetermined image processing (for example, binarization processing) on the lubricating grease image information in the spindle bearing acquired by the imaging unit 43 (imaging device). ) To calculate the degree of contamination of the foreign matter in the lubricating grease, and diagnose the abnormality sign of the spindle bearing based on the current degree of abnormality calculated according to the degree of contamination (herein referred to as the first degree of abnormality). Can do.

  FIG. 6 is a diagram illustrating a method for predicting the replacement timing of the spindle bearing in the predictive diagnosis mode unit. The horizontal axis indicates the cumulative number of rotations of the spindle bearing from the mode switching point (when it is determined as an abnormal sign by the lubrication grease), and the vertical axis indicates the degree of abnormality (for example, vibration value). The initial value of the degree of abnormality is a vibration value at the time when it is diagnosed that there is a sign of abnormality of the spindle bearing acquired in step S46 (see FIG. 4).

The degree of abnormality in the predictive diagnosis mode is based on the following prediction formula (1).
(Abnormality) = A × N ^ (C) + Initial value (1)
A: Constant 1
C: Constant 2
N: Cumulative rotational speed of spindle bearing from mode switching point N ^ (C) means N to the C power (power). The degree of abnormality is a vibration value of the main shaft bearing, and its unit is (dB). Moreover, the constants A and C are numerical values that vary depending on the type of wind power generation system, location conditions, and the like, and are set in advance according to past operation results.

  In FIG. 6, the prediction formula is different for each model (for example, model X, Y, Z), and the abnormality determination threshold is different for each model. In the predictive diagnosis mode unit 24, until the degree of abnormality reaches the abnormality determination threshold, the reference rotation number for each month (see FIG. 7) is accumulated from the mode switching point, and the cumulative number of rotations when the abnormality determination threshold is reached. Calculate the number of months until N is reached.

  That is, the predictive diagnosis mode unit 24 of the management device 100 acquires the vibration value of the main shaft bearing and sets it as the vibration initial value, and when a predetermined time has elapsed from the start of operation of the wind power generation facility (for example, abnormal lubrication grease) Based on the cumulative number of rotations of the main shaft of the wind power generation facility 10 estimated from the point in time determined as a predictor and the initial vibration value at that time, an abnormal sign of the main shaft bearing is diagnosed in the future, and the replacement timing of the main shaft bearing is predicted. To do.

  Specifically, the management apparatus 100 sets the predetermined number of rotations in the predictive diagnosis mode unit 24 by adding up the monthly reference rotation speeds (see FIG. 7) corresponding to the respective months elapsed from the mode switching point (switching point). A future abnormality that is calculated by adding the calculation result obtained by performing the calculation (for example, A × N ^ (C) of the prediction formula (1)) to the initial vibration value (for example, the initial value of the prediction formula (1)). Diagnose abnormal signs of spindle bearings based on the degree.

  The management device 100 diagnoses that it is time to replace the spindle bearing when the future abnormality degree reaches a second reference abnormality degree (for example, an abnormality determination threshold).

  FIG. 7 is a diagram illustrating an example of each month-based rotation speed, where (a) is an example when operating throughout the year, and (b) is an example when there is a non-operation period. The wind power generator of the wind power generation facility 10 is characterized in that the number of revolutions varies greatly depending on the season. As in the example of FIG. 7A, there is a difference in the rotational speed due to seasonal changes in wind speed. Japan tends to be stronger in winter, due to the strong seasonal winds from the winter continent. Moreover, the wind speed fluctuates with the short-term change as well as the long-term annual average wind speed. This is due to weather changes and climate change in each year, and is generally said to change within a range of 10% of the average value (average value for 30 years). In the case of FIG. 7B, there is a non-operational period due to the influence of regular inspections and the like in June.

  In the present embodiment, the monthly reference rotational speed is the sum of the rotational speeds of the main spindle bearings stored in the month in the last operation year (excluding the non-operating year) of the wind power generation facility 10. It is good to do.

  FIG. 8 is a diagram illustrating an example of a prediction diagnosis result displayed on the display unit 42 of the management apparatus 100. The horizontal axis in FIG. 8 indicates the cumulative number of revolutions after the lubricant grease abnormality sign determination (after mode switching), and the vertical axis indicates the average amplitude value of the AE wave. FIG. 8 shows a situation where a predetermined period has elapsed after the mode switching and 6.8 million revolutions have occurred. In the prediction of the driveable time, the remaining months until the threshold is output.

  That is, the management device 100 includes a display unit 42 (display device), and when the management device 100 diagnoses that it is time to replace the spindle bearing, the management device 100 displays the elapsed months from the switching point on the display device. Can do. Thereby, the service person can determine the periodic replacement time in a planned manner and can adjust the stop time of the wind power generation facility to the user early.

  The abnormality sign diagnosis system for the wind power generation facility 10 according to the present embodiment includes the wind power generation facility 10 and a management device 100, and the management device 100 is a main shaft bearing (for example, main shaft bearings 13 and 14) of the wind power generation facility 10. The current diagnosis mode unit 23 (first diagnosis mode unit) for diagnosing an abnormality sign of the main shaft bearing at that time according to the state of the grease, and acquiring the vibration value of the main shaft bearing as the initial vibration value, the initial vibration value And predicting an abnormal sign of the spindle bearing in the future based on the accumulated rotational speed of the main shaft of the wind power generation facility 10 estimated from a point in time (for example, a mode switching point) after the start of operation of the wind power generation facility. And a predictive diagnosis mode unit 24 for predicting the replacement time of the bearing. Thereby, it is possible to accurately predict the replacement time of the spindle bearing.

  The management apparatus 100 of the wind power generation facility 10 includes a current diagnosis mode unit 23 for diagnosing an abnormal sign of the main shaft bearing at that time according to a state of grease of the main shaft bearing of the wind power generation facility 10, and a vibration value of the main shaft bearing. Obtain an initial value of vibration and obtain an abnormal sign of the main shaft bearing in the future based on the initial value of vibration and the accumulated rotational speed of the main shaft of the wind power generation facility 10 estimated from the time when a predetermined time has elapsed from the start of operation of the wind power generation facility. And a predictive diagnosis mode unit 24 for diagnosing and predicting the replacement time of the main shaft bearing. According to the management device 100 of the present embodiment, it is possible to accurately predict the replacement timing of the spindle bearing even if there is an individual difference in the wind power generation facility 10.

  The abnormality sign diagnosis method for the management apparatus 100 of the wind power generation facility 10, wherein the management apparatus 100 diagnoses the abnormality sign of the main shaft bearing at that time according to the grease state of the main shaft bearing of the wind power generation facility 10. Step, obtaining the vibration value of the main shaft bearing and setting it as the initial vibration value, and the initial value of the vibration and the accumulated rotational speed of the main shaft of the wind power generation facility 10 estimated from when a predetermined time has elapsed from the start of operation of the wind power generation facility 10; And a predictive diagnosis step of diagnosing a sign of an abnormality of the main shaft bearing in the future and predicting a replacement time of the main shaft bearing. According to the abnormality sign diagnosis method of the present embodiment, it is possible to accurately predict the replacement timing of the spindle bearing in consideration of individual differences even in the same model. In addition, since the replacement time can be known early, it is possible to determine the time for periodic inspection with the user at an early stage.

DESCRIPTION OF SYMBOLS 1 Hub 2 Blade 3 Nacelle 4 Tower 5 Generator 6 Stator 7 Generator rotor 8 Rotor plate 9 Stator casing 10 Wind power generation equipment 11 Main shaft (first main shaft)
12 Spindle (second spindle)
13 Spindle bearing (main bearing, first spindle bearing)
14 Spindle bearing (main bearing, second spindle bearing)
DESCRIPTION OF SYMBOLS 15 3rd spindle bearing 16 Protrusion part 19 Stator casing 20 Processing part 21 Sensor data acquisition part 22 Operation data acquisition part 23 Current diagnosis mode part (1st diagnosis mode part)
24 Predictive diagnosis mode section 30 Storage section 31 Lubrication grease image 32 Lubrication grease determination result 33 Sensor data 34 Operation data 35 Monthly reference rotation speed 36 Replacement time prediction result 41 Input section 42 Display section (display device)
43 Imaging unit (imaging device)
44 Communication Unit 50 Sensor 51 Amplifier 52 Data Collection Device 100 Management Device NW Network

Claims (9)

A wind power generation facility and a management device,
The management device
A current diagnosis mode unit for diagnosing an abnormal sign of the main shaft bearing at that time according to a state of grease of the main shaft bearing of the wind power generation facility;
The vibration value of the main shaft bearing is acquired as an initial vibration value, and the initial value of the vibration and the cumulative rotational speed of the main shaft of the wind power generation facility estimated from the time when a predetermined time has elapsed from the start of operation of the wind power generation facility. A diagnostic diagnosis mode unit for diagnosing future signs of abnormality of the spindle bearing based on the future and predicting the replacement timing of the spindle bearing,
The management device
In the current diagnosis mode unit, predetermined image processing is performed on the image information of the grease in the spindle bearing acquired by the imaging device to calculate the degree of contamination of the foreign matter in the grease, and the calculation is performed according to the degree of contamination. Diagnosing an abnormality sign of the spindle bearing based on the first abnormality degree;
The diagnosis of the first abnormality degree of the current diagnostic mode portion is abnormal sign of the spindle bearing when it reaches the first reference abnormality degree, wind force you, characterized in that the process proceeds to the prediction diagnostic mode portion Abnormality sign diagnosis system for power generation facilities. A wind power generation facility and a management device,
The management device
A current diagnosis mode unit for diagnosing an abnormal sign of the main shaft bearing at that time according to a state of grease of the main shaft bearing of the wind power generation facility;
The vibration value of the main shaft bearing is acquired as an initial vibration value, and the initial value of the vibration and the cumulative rotational speed of the main shaft of the wind power generation facility estimated from the time when a predetermined time has elapsed from the start of operation of the wind power generation facility. A diagnostic diagnosis mode unit for diagnosing future signs of abnormality of the spindle bearing based on the future and predicting the replacement timing of the spindle bearing,
Wherein when a predetermined time period from the operation start of the wind power installation has elapsed, the abnormality sign diagnosis system wind power generation facilities you being a current switch point of transition from the diagnostic mode portion to said prediction diagnostic mode portion . The management device
In the prediction diagnosis mode unit, a calculation result obtained by performing a predetermined calculation on the cumulative rotation number obtained by accumulating each month reference rotation number corresponding to each month passed from the switching point is added to the vibration initial value to calculate. The abnormality sign diagnosis system for wind power generation equipment according to claim 2 , wherein an abnormality sign of the spindle bearing is diagnosed based on a degree of abnormality in the future. The management device
The abnormality predictor diagnosis system for wind power generation equipment according to claim 3 , wherein when the future abnormality degree reaches a second reference abnormality degree, it is diagnosed that it is time to replace the spindle bearing. The monthly reference rotational speed is the total value of the rotational speeds of the spindle bearings stored last month in the operating year (excluding the non-operating year) of the wind power generation facility in the past. The abnormality sign diagnosis system for wind power generation equipment according to claim 3 , characterized in that: The management device includes a display device,
The abnormality of the wind power generation facility according to claim 4 , wherein when the management device diagnoses that it is time to replace the spindle bearing, the display device displays the number of months elapsed from the switching point. Predictive diagnosis system. A management device for wind power generation equipment,
A current diagnosis mode unit for diagnosing an abnormality sign of the main shaft bearing at that time according to a state of grease of the main shaft bearing of the wind power generation facility,
The vibration value of the main shaft bearing is acquired as an initial vibration value, and the initial value of the vibration and the cumulative rotational speed of the main shaft of the wind power generation facility estimated from the time when a predetermined time has elapsed from the start of operation of the wind power generation facility. based diagnose abnormal sign of the main shaft bearing in the future, have a, and prediction diagnostic mode unit that predicts time for replacement of the main shaft bearing,
The management device
In the current diagnosis mode unit, predetermined image processing is performed on the image information of the grease in the spindle bearing acquired by the imaging device to calculate the degree of contamination of the foreign matter in the grease, and the calculation is performed according to the degree of contamination. Diagnosing an abnormality sign of the spindle bearing based on the first abnormality degree;
The management apparatus , wherein when the first abnormality degree of the current diagnosis mode unit reaches a first reference abnormality degree, it is diagnosed that there is an abnormality sign of the spindle bearing, and the process proceeds to the prediction diagnosis mode unit . A management device for wind power generation equipment,
A current diagnosis mode unit for diagnosing an abnormality sign of the main shaft bearing at that time according to a state of grease of the main shaft bearing of the wind power generation facility,
The vibration value of the main shaft bearing is acquired as an initial vibration value, and the initial value of the vibration and the cumulative rotational speed of the main shaft of the wind power generation facility estimated from the time when a predetermined time has elapsed from the start of operation of the wind power generation facility. based diagnose abnormal sign of the main shaft bearing in the future, have a, and prediction diagnostic mode unit that predicts time for replacement of the main shaft bearing,
The management device according to claim 1, wherein a point in time when a predetermined time has elapsed from the start of operation of the wind power generation facility is a switching point at which the current diagnosis mode unit shifts to the prediction diagnosis mode unit . An abnormality sign diagnosis method for a wind turbine generator management device,
The management device
A current diagnosis step of diagnosing an abnormal sign of the main shaft bearing at that time according to a state of grease of the main shaft bearing of the wind power generation facility;
The vibration value of the main shaft bearing is acquired as an initial vibration value, and the initial value of the vibration and the cumulative rotational speed of the main shaft of the wind power generation facility estimated from the time when a predetermined time has elapsed from the start of operation of the wind power generation facility. A predictive diagnosis step of diagnosing a future sign of an abnormality of the main shaft bearing based on the prediction and predicting a replacement timing of the main shaft bearing.