Patent Publication Number: US-2022213872-A1

Title: Condition monitoring system and wind power generation system including the same

Description:
TECHNICAL FIELD 
     The present invention relates to a condition monitoring system and a wind power generation system comprising the same, and more specifically to a condition monitoring system capable of monitoring a condition of an apparatus that is a constituent of a wind power generation facility or the like and a wind power generation system employing the same. 
     BACKGROUND ART 
     Japanese Patent Laid-Open No. 2013-185507 discloses a condition monitoring system (CMS) for monitoring an abnormality of an apparatus provided for a wind power generation facility. The condition monitoring system uses data that satisfies a predetermined operating condition to generate a threshold value used for abnormality diagnosis of the apparatus, and uses the generated threshold value to perform abnormality diagnosis for the apparatus (see PTL 1). 
     CITATION LIST 
     Patent Literature 
     PTL 1: Japanese Patent Laid-Open No. 2013-185507 
     SUMMARY OF INVENTION 
     Technical Problem 
     The condition monitoring system described in PTL 1 does not use data which does not satisfy the operating condition in generating the threshold value, and the system is not fully exploited to monitor the condition of the apparatus (such as for abnormality diagnosis). However, for example, wind power generation and other similar facilities are affected by natural environments, and accordingly, there are also many periods of time for which operating conditions (such as the rotational speed of the main shaft and the amount of power generated by the power generator) are unsatisfied. Therefore, there is a possibility that the above condition monitoring system may not monitor a condition for many periods of time, resulting in delayed condition monitoring (or abnormality diagnosis). 
     The present invention has been made to address such an issue, and an object of the present invention is to provide a condition monitoring system capable of effectively utilizing measurement data collected under various operating conditions to monitor a condition of an apparatus, and a wind power generation system comprising the condition monitoring system. 
     Solution to Problem 
     A condition monitoring system according to the present invention comprises a measurement device and a processor. The measurement device measures a condition of an apparatus provided for a facility. The processor associates measurement data measured by the measurement device with load data representing an operating load of the facility acting at a time when the measurement data is measured and cumulative load data representing a cumulative operating load accumulated up to the time when the measurement data is measured, to generate a data set of the load data, the cumulative load data, and the measurement data for the time when the measurement data is measured. 
     The present condition monitoring system allows measurement data measured by the measurement device to be associated with cumulative load data that may represent a point in time when the measurement data is measured, and to be also associated with load data obtained at the point in time when the measurement data is measured, to generate a data set of the load data, the cumulative load data, and the measurement data, and the condition monitoring system allows analysis of condition monitoring with an operating load also considered. That is, measurement data is not selected or excluded depending on the facility&#39;s operating condition (or operating load), and any collected data (or data set) can be used to monitor a condition of the facility. Thus, the condition monitoring system can effectively use measurement data collected under various operating conditions to monitor the condition of the apparatus. 
     Further, the above condition monitoring system uses a cumulative load, rather than a data measurement time, and can exclude data obtained for a period of time for which the facility is stopped. As a result, when data interpolation, function fitting, and the like are performed, discontinuity of data can be eliminated to increase accuracy. 
     Preferably, the processor collects the measurement data and the load data periodically or aperiodically. Furthermore, the processor is further configured to subject a plurality of data sets each generated based on the collected measurement data and load data to data interpolation to generate interpolated three-dimensional data with the cumulative load data, the load data, and the measurement data represented along a first axis, a second axis, and a third axis, respectively. 
     Preferably, the processor is further configured to: use the interpolated three-dimensional data to calculate a relationship between the load data and the measurement data for any past point in time; and use the calculated past relationship to estimate a relationship between the load data and the measurement data for a current point in time. 
     Preferably, the processor is further configured to: use the interpolated three-dimensional data to calculate a relationship between the cumulative load data and the measurement data for a plurality of the load data different in magnitude from one another; use a result of the calculation to predict a value of the measurement data for any future point in time for each of the plurality of load data; and use a result of the prediction to estimate a relationship between the load data and the measurement data for the future point in time. 
     Preferably, the processor is further configured to: use the interpolated three-dimensional data to calculate a relationship between the cumulative load data and the measurement data with the load data set to a predetermined value; and use a result of the calculation to predict a magnitude of the cumulative load data for which the measurement data exceeds a threshold value. 
     A wind power generation system according to the present invention comprises a wind power generation facility and any one of the condition monitoring systems described above. The condition monitoring system monitors a condition of an apparatus that is a constituent of the wind power generation facility. 
     Advantageous Effects of Invention 
     The present invention can thus provide a condition monitoring system capable of effectively utilizing measurement data collected under various operating conditions to monitor a condition of an apparatus, and a wind power generation system comprising the condition monitoring system. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram schematically showing an overall configuration of a condition monitoring system according to an embodiment. 
         FIG. 2  is a diagram schematically showing a configuration of a wind power generation facility. 
         FIG. 3  is a block diagram functionally showing a configuration of a processing server. 
         FIG. 4  is a flowchart of an example of a procedure of a process performed in a data calculation unit. 
         FIG. 5  plots on a three-dimensional graph each data set generated in step S 20  of  FIG. 4 . 
         FIG. 6  represents interpolated three-dimensional data generated from each data shown in  FIG. 5 . 
         FIG. 7  represents a profile representing a relationship between rotational speed and degree of vibration for some cumulative number of rotations. 
         FIG. 8  represents a profile representing a relationship between cumulative number of rotations and degree of vibration for some rotational speed. 
         FIG. 9  is a flowchart of an example of a procedure of a process performed in an estimation unit. 
         FIG. 10  is a diagram for describing an idea of estimating a current value. 
         FIG. 11  is a flowchart of an example of a procedure of a process performed in step S 220  of  FIG. 9  to estimate a current value. 
         FIG. 12  is a diagram for describing an idea of estimating a predicted value. 
         FIG. 13  is a flowchart of an example of a procedure of a process performed in step S 250  of  FIG. 9  to estimate a predicted value. 
         FIG. 14  is a flowchart of an example of a procedure of a process for predicting a future point in time (or a cumulative number of rotations) for which a degree of vibration exceeds a threshold value. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter reference will be made to the drawings to describe the present invention in an embodiment. In the figures, identical or corresponding components are identically denoted and will not be described repeatedly. 
     &lt;General Configuration of Condition Monitoring System&gt; 
       FIG. 1  is a diagram generally showing a configuration of a condition monitoring system according to the present embodiment. Referring to  FIG. 1 , the condition monitoring system includes a measurement device  80 , a processing server  330 , and a monitoring terminal  340 . 
     Measurement device  80  is provided to a wind power generation facility  10  and calculates an effective value, a peak value, a crest factor, an effective value after envelope processing, a peak value after envelope processing, etc. from sensed values received from a variety of sensors described hereinafter, and transmits them to processing server  330  via the Internet  320 . While in this example measurement device  80  and processing server  330  perform communications through a wire, they may do so wirelessly. 
     Processing server  330  includes a CPU (central processing unit), a ROM (read only memory), a RAM (random access memory), and a communication device for communicating with measurement device  80  of wind power generation facility  10  via Internet  320  (all not shown). The CPU loads a program stored in the ROM into the RAM or the like and executes the program. The program stored in the ROM is a program describing a procedure of a process performed by server  330 . The process performed by processing server  330  will be described in detail hereinafter. 
     Monitoring terminal  340  is connected to processing server  330  for example by an in-house LAN (Local Area Network). Monitoring terminal  340  is provided to display a condition of each apparatus of wind power generation facility  10 , browse data analyzed in processing server  330 , input and change various settings in processing server  330 , and the like. 
     &lt;Configuration of Wind Power Generation Facility&gt; 
       FIG. 2  is a diagram schematically showing a configuration of wind power generation facility  10 . Referring to  FIG. 2 , wind power generation facility  10  includes a main shaft  20 , a blade  30 , a gearbox  40 , a power generator  50 , a main shaft bearing  60 , a nacelle  90 , and a tower  100 . Wind power generation facility  10  further includes sensors  70 A to  70 H and a measurement device  80 . Gearbox  40 , power generator  50 , main shaft bearing  60 , sensors  70 A to  70 H, and measurement device  80  are housed in nacelle  90 , and nacelle  90  is supported by tower  100 . 
     Main shaft  20  is inserted into nacelle  90 , connected to the input shaft of gearbox  40 , and is rotatably supported by main shaft bearing  60 . And main shaft  20  transmits rotational torque that is generated by blade  30  receiving wind power to the input shaft of gearbox  40 . Blade  30  is provided at an end of main shaft  20 , converts wind power into rotational torque, and transmits it to main shaft  20 . 
     Main shaft bearing  60  is disposed in nacelle  90  in a fixed manner and supports main shaft  20  rotatably. Main shaft bearing  60  is a rolling bearing, and for example, it is a self-centering roller bearing, a conical roller bearing, a cylindrical roller bearing, a ball bearing, etc. Note that these bearings may be of a single row or double rows. 
     Sensors  70 A- 70 H are disposed in nacelle  90  at each apparatus in a fixed manner. Specifically, sensor  70 A is disposed on main shaft bearing  60  in a fixed manner and monitors a condition of main shaft bearing  60 . Sensors  70 B- 70 D are disposed on gearbox  40  in a fixed manner, and monitor a condition of gearbox  40 . Sensors  70 E and  70 F are disposed on power generator  50  in a fixed manner, and monitor a condition of power generator  50 . Sensor  70 G is disposed on main shaft bearing  60  in a fixed manner, and monitors misalignment, and abnormal vibration of nacelle  90 . Sensor  70 H is disposed on main shaft bearing  60  in a fixed manner, and monitors unbalance, and abnormal vibration of the nacelle. 
     Gearbox  40  is provided between main shaft  20  and power generator  50  to increase the rotational speed of main shaft  20  and output the increased rotational speed to power generator  50 . As an example, gearbox  40  is composed of a gear speed-up mechanism including a planetary gear, a countershaft, a high speed shaft, etc. Note that although not shown in the figure, gearbox  40  is also provided therein with a plurality of bearings to support a plurality of shafts rotatably. Power generator  50  is connected to the output shaft of gearbox  40 , and generates electric power by the rotational torque received from gearbox  40 . Power generator  50  is an induction generator, for example. Note that power generator  50  is also provided therein with a bearing to support a rotor rotatably. 
     Measurement device  80  receives measurement data of each apparatus&#39;s vibration, acoustic emission (AE), temperature, operating sound and other measurement data sensed by sensors  70 A- 70 H. Measurement device  80  periodically transmits a variety of types of measurement data to processing server  330  via Internet  320 . 
     The measurement data may be converted into parameters that can better indicate conditions of apparatuses. For example, each measurement data may be converted into an effective value, a peak value, a crest factor, an effective value after envelope processing, a peak value after envelope processing, or the like, and measurement device  80  may transmit the measurement data converted into any one of the above values to processing server  330  via Internet  320 . As an example, sensor  70 A is a vibration sensor that senses vibration of main shaft bearing  60 , and measurement device  80  calculates an effective value of vibration (hereinafter referred to as a “degree of vibration”) of main shaft bearing  60  from data measured by sensor  70 A, and transmits the degree of vibration to processing server  330 . 
     Measurement device  80  may periodically store the measurement data to a storage device located in measurement device  80  and send the stored measurement data to processing server  330  via Internet  320  in response to a request received from processing server  330 . 
     &lt;Condition Monitoring&gt; 
     Wind power generation facility  10  is affected by natural environments, and its operating load (such as the rotational speed of the main shaft and the amount of power generated by the power generator) varies. Conventionally, as in the condition monitoring system described in PTL 1, data satisfying a predetermined operating condition has been used to monitor a condition of an apparatus (such as to diagnose abnormality). In other words, measurement data which does not satisfy the operating condition is not used for condition monitoring and a cost of measuring data is wasted, and there is also a possibility that condition monitoring (or abnormality diagnosis) may be delayed. 
     Accordingly, the condition monitoring system according to the present embodiment allows measurement data measured by measurement device  80  to be associated with cumulative load data that may represent a point in time when the measurement data is measured, and to be also associated with load data obtained at the point in time when the measurement data is measured, to generate a data set of the load data, the cumulative load data, and the measurement data. Such a data set is created whenever data is collected, and a plurality of data sets thus generated is subjected to data interpolation to generate interpolated three-dimensional data with the cumulative load data, the load data, and the measurement data represented along a first axis, a second axis, and a third axis, respectively. 
     Thus using the interpolated three-dimensional data also considering load data obtained at a point in time when measurement is conducted enables analysis of condition monitoring also considering an operating load of wind power generation facility  10 . That is, measurement data is not selected or excluded depending on an operating condition (or an operating load) of wind power generation facility  10 , and basically, any measured data (excluding abnormality data, noise and the like) can be used to monitor a condition of wind power generation facility  10 . 
     Thus, although details will be described hereinafter, for example, interpolated three-dimensional data can be used to estimate a relationship between the operating load and the measurement data for a current point in time, and the estimated relationship can be used to estimate measurement data for any operating load at the current point in time (i.e., to estimate a current value). Further, interpolated three-dimensional data can be used to predict a relationship between the operating load and the measurement data for any future point in time, and the predicted relationship can be used to estimate measurement data for any operating load at the future point in time (i.e., to estimate a predicted value). Furthermore, it is also possible to estimate when the measurement data exceeds a threshold value for any operating load (i.e., to estimate a cumulative load). These will be described hereinafter in detail. 
     Further, the condition monitoring system according to the present embodiment uses a cumulative load, rather than a data measurement time, and can exclude data obtained for a period of time for which wind power generation facility  10  is stopped. As a result, when data interpolation, function fitting, and the like are performed, discontinuity of data can be eliminated to increase accuracy. 
     Each above process is performed by processing server  330 . Hereinafter, the process performed by processing server  330  will be described in detail. In the following description, load data is a rotational speed (in rpm) of main shaft  20  (or blade  30 ), cumulative load data is a cumulative number of rotations (in times) of main shaft  20  (or blade  30 ), and measurement data is a degree of vibration of main shaft bearing  60 . 
       FIG. 3  is a block diagram functionally representing a configuration of processing server  330 . Referring to  FIG. 3 , processing server  330  includes a data collection unit  120 , a data storage unit  130 , a data calculation unit  140 , and an estimation unit  150 . 
     Data collection unit  120  periodically collects data of a degree of vibration of main shaft bearing  60  (i.e., measurement data) from measurement device  80  ( FIG. 2 ) via Internet  320 . Further, as data collection unit  120  receives the data of the degree of vibration, data collection unit  120  also collects data of a rotational speed of main shaft  20  (i.e., load data) synchronous with the degree of vibration. 
     Data collection unit  120  stores the data of the degree of vibration received from measurement device  80  to data storage unit  130  in time series together with the data of the rotational speed. Data collection unit  120  may collect the data periodically in an automated manner as described above, or manually by an operator of monitoring terminal  340 . When the data is collected manually by the operator, the data is temporarily stored in measurement device  80  of wind power generation facility  10 , and in response to a request from the operator, the data stored in measurement device  80  is collectively transmitted to processing server  330  and stored to data storage unit  130 . 
     Data storage unit  130  synchronizes the data of the degree of vibration and that of the rotational speed as collected by data collection unit  120  and thus stores them in time series. Furthermore, data storage unit  130  stores a data set generated by data calculation unit  140 , as will be described hereinafter, and interpolated three-dimensional data. Data storage unit  130  is composed for example of a large-capacity storage device such as a hard disk drive (HDD) or a solid state drive (SSD). 
     Data calculation unit  140  processes the data of the degree of vibration and the rotational speed that are stored in data storage unit  130  to generate a data set of the rotational speed of main shaft  20  (or load data), a cumulative number of rotations (or cumulative load data) of main shaft  20 , and the degree of vibration (or measurement data) of main shaft bearing  60 . This data set is generated for all collected data. Data calculation unit  140  then uses a plurality of such data sets to generate interpolated three-dimensional data with the cumulative number of rotations, the rotational speed and the degree of vibration represented along a first axis, a second axis, and a third axis, respectively. Hereinafter, the process by data calculation unit  140  will be described in more detail. 
       FIG. 4  is a flowchart of an example of a procedure of the process performed in data calculation unit  140 . Referring to  FIG. 4 , data calculation unit  140  obtains data of the degree of vibration and that of the rotational speed that are stored in data storage unit  130  in chronological order (step S 10 ). 
     Subsequently, whenever data calculation unit  140  obtains such data successively, data calculation unit  140  calculates a cumulative number of rotations from the data of the rotational speed. The data of the rotational speed is data collected periodically as prescribed, and hence regularly, and can be accumulated to calculate a cumulative number of rotations. Data calculation unit  140  associates the calculated cumulative number of rotations with the data of the rotational speed and that of the degree of vibration that are obtained to generate a data set of the rotational speed, the cumulative number of rotations, and the degree of vibration. Whenever data calculation unit  140  obtains data of a degree of vibration and data of a rotational speed from data storage unit  130  successively, data calculation unit  140  processes the data as described above to generate a data set described above (step S 20 ). 
       FIG. 5  plots on a three-dimensional graph each data set generated in step S 20  of  FIG. 4 . Referring to  FIG. 5 , a first axis (the X axis) represents the cumulative number of rotations, a second axis (the Y axis) represents the rotational speed, and a third axis (the Z axis) represents the degree of vibration. Each point on the three-dimensional graph represents a value of a data set. 
     As the first axis represents the cumulative number of rotations, there is no discontinuity appearing if the first axis represents time (that is, there is no period of time that continues for which the rotational speed and the degree of vibration each have a value of approximately 0 while rotation is stopped), and accuracy of data interpolation and function fitting, which will be described hereinafter, can be increased. 
     Referring again to  FIG. 4 , data calculation unit  140  performs an interpolation process for each data shown in  FIG. 5  to generate interpolated three-dimensional data with the cumulative number of rotations, the rotational speed, and the degree of vibration represented along the first axis, the second axis, and the third axis, respectively (step S 30 ). 
       FIG. 6  represents interpolated three-dimensional data generated from each data shown in  FIG. 5 . Referring to  FIG. 6 , as well as  FIG. 5 , the first axis (the X axis) represents the cumulative number of rotations, the second axis (the Y axis) represents the rotational speed, and the third axis (the Z axis) represents the degree of vibration. The method per se employed to interpolate the  FIG. 5  scattered data to generate the interpolated three-dimensional data as shown can be a variety of known methods, and for example, polynomial approximation can be employed to interpolate the data. 
     When additional data is collected by data collection unit  120 , data calculation unit  140  updates the interpolated three-dimensional data. In order to reduce a processing load, the interpolated three-dimensional data may not be updated whenever additional data is collected, and it may be updated less frequently, as appropriate. 
     Referring again to  FIG. 4 , data calculation unit  140  operates in response to a request received from the operator of monitoring terminal  340  to perform an output process to output a variety of types of data to a screen of monitoring terminal  340  (step S 40 ). By this output process, for example, a diagram in which each data set is plotted, a three-dimensional graph (see  FIG. 5 ) or interpolated three-dimensional data in which data is interpolated (see  FIG. 6 ), and the like can be output to the screen of monitoring terminal  340 . Further, based on the interpolated three-dimensional data generated in step S 30 , the profile of the degree of vibration in any cross section (a YZ plane with a fixed cumulative number of rotations or an XZ plane with a fixed rotational speed) can be output. 
       FIG. 7  represents a profile representing a relationship between the rotational speed and the degree of vibration for some cumulative number of rotations.  FIG. 8  represents a profile representing a relationship between the cumulative number of rotations and the degree of vibration for some rotational speed. Thus, by designating a cumulative number of rotations or a rotational speed, a profile of a degree of vibration for any cross section can be output to the screen of monitoring terminal  340 . 
     Referring to  FIG. 3  again, estimation unit  150  is configured to be capable of using the interpolated three-dimensional data generated in data calculation unit  140  to perform a current-value estimation process for estimating a degree of vibration (or measurement data) for any rotational speed (or operating load) at the current point in time (or for the current cumulative number of rotations). Further, estimation unit  150  is configured to be capable of using the above interpolated three-dimensional data to perform a predicted-value estimation process for predicting a degree of vibration for any rotational speed at any future point in time. Hereinafter, the process performed by estimation unit  150  will be described more specifically. 
       FIG. 9  is a flowchart of an example of a procedure of a process performed in estimation unit  150 . Referring to  FIG. 9 , estimation unit  150  determines whether to perform the current-value estimation to estimate a degree of vibration for any rotational speed at the current point in time (step S 210 ). For example, the unit determines to perform the current-value estimation when a request is received from monitoring terminal  340  to do so. For NO in step S 210 , the process proceeds to step S 240 . 
     For YES in step S 210 , estimation unit  150  performs the current-value estimation process (step S 220 ). Hereinafter, the current-value estimation process will be described in detail. 
     &lt;Current-Value Estimation Process&gt; 
       FIG. 10  is a diagram for describing an idea of the current-value estimation. 
     Referring to  FIG. 10 , the horizontal axis represents rotational speed, and the vertical axis represents degree of vibration. A line k 1  shows a profile which is extracted from generated interpolated three-dimensional data and which represents the relationship between the rotational speed and the frequency for any past cumulative number of rotations t 1 . A line k 2  shows a profile which is extracted from the interpolated three-dimensional data and which represents the relationship between the rotational speed and the frequency for any past cumulative number of rotations t 2 . 
     A dotted line k 3  represents an estimated profile representing the relationship between the rotational speed and the frequency for the current cumulative number of rotations to (or the current point in time). Dotted line k 3  is estimated as follows. Initially, a functional form for dotted line k 3  is determined based on lines k 1  and k 2 . That is, a functional form for the profile at the current point in time is determined with reference to the shape of a past profile. The function may be a polynomial or employ an exponential function. The functional form is determined by the operator of monitoring terminal  340 . The number of past profiles used in determining the functional form is not limited to two profiles (of lines k 1  and k 2 ), and may be one or three or more profiles. 
     And recently obtained data (in this example, Pc 1  to Pc 3 ) are used to perform fitting of dotted line k 3 . Dotted line k 3  thus obtained represents a profile representing the relationship between the rotational speed and the frequency for the current cumulative number of rotations to (or the current point in time), and for example allows a degree of vibration (a point P 1 ) for any rotational speed to be estimated at the current point in time. 
       FIG. 11  is a flowchart of an example of a procedure of the current-value estimation process performed in step S 220  of  FIG. 9 . Referring to  FIG. 11  together with  FIG. 10 , estimation unit  150  sets any reference cumulative number of rotations (step S 310 ). The reference cumulative number of rotations is, for example, any past cumulative numbers of rotations t 1  and t 2  described with reference to  FIG. 10 , and may be set by the operator of monitoring terminal  340  or may be automatically set by estimation unit  150  as desired. 
     Subsequently, estimation unit  150  obtains the data (the rotational speed and degree of vibration) for the reference cumulative number of rotations from the interpolated three-dimensional data calculated by data calculation unit  140 , and causes monitoring terminal  340  to display the data (step S 315 ). 
     Then, a functional form is set based on the data displayed in step S 315  (step S 320 ). The functional form is set by the operator of monitoring terminal  340 . Once the functional form has been set, estimation unit  150  uses the data obtained and displayed in step S 315  to fit the function set in step S 320  (step S 325 ). Fitting the function can be done for example through least squares. 
     Thereafter, estimation unit  150  causes monitoring terminal  340  to display the fitted function together with the data used to fit the function (step S 330 ). Estimation unit  150  then determines whether to apply the functional form set in step S 320  (step S 335 ). The functional form is actually determined by the operator of monitoring terminal  340 , and estimation unit  150  determines that the functional form set in step S 320  is applied when monitoring terminal  340  receives an input to determine that the functional form is applied. 
     If the function is insufficiently fitted and no functional form is determined (NO in step S 335 ), the process returns to step S 310 , and steps S 310  to S 330  are performed. 
     When a functional form is determined in step S 335  (YES in step S 335 ), estimation unit  150  sets the number of recent reference data or a recent range (e.g., a range of cumulative numbers of rotations for which the recent reference data is obtained) (step S 340 ). This sets a condition for extracting reference data (in  FIG. 10 , Pc 1  to Pc 3 ) used for fitting dotted line k 3  shown in  FIG. 10 . Estimation unit  150  then obtains recent data (for example, Pc 1  to Pc 3  in  FIG. 10 ) from data storage unit  130  in accordance with the condition set in step S 340  for extraction (step S 345 ). 
     Subsequently, estimation unit  150  uses the obtained reference data to fit the function determined in step S 335  (step S 350 ). In this case as well, for example, fitting the function can be done for example through least squares. Then, estimation unit  150  causes monitoring terminal  340  to display the fitted function together with the reference data used for fitting the function (step S 355 ). 
     Subsequently, estimation unit  150  obtains a rotational speed as a target for estimation (step S 360 ). The rotational speed as a target for estimation corresponds to the rotational speed of point P 1  that is set by the operator of monitoring terminal  340  and for which estimating a degree of vibration is desired, as shown in  FIG. 10 . Estimation unit  150  uses the fitted function to estimate a degree of vibration for the rotational speed as the target for estimation (i.e., perform the current-value estimation) (step S 365 ). 
     Referring to  FIG. 9  again, when the current-value estimation process is performed in step S 220 , estimation unit  150  outputs to monitoring terminal  340  an estimation result obtained through the current-value estimation process, that is, a profile of a function representing a relationship between the rotational speed and the degree of vibration for the current point in time (or the current cumulative number of rotations), as well as the rotational speed set as the target for estimation and an estimated value of a degree of vibration for the rotational speed set as the target for estimation (step S 230 ). 
     Subsequently, estimation unit  150  determines whether to estimate a degree of vibration for any rotational speed at any future point in time (i.e., to perform predicted-value estimation) (step S 240 ). For example, the unit determines to perform the predicted-value estimation when a request is received from monitoring terminal  340  to do so. For NO in step S 240 , the process ends. 
     For YES in the step S 240 , estimation unit  150  performs the predicted-value estimation process (step S 250 ). Hereinafter, the predicted-value estimation process will be described in detail. 
     &lt;Predicted-Value Estimation Process&gt; 
       FIG. 12  is a diagram for describing an idea of the predicted-value estimation. Referring to  FIG. 12 , the horizontal axis represents the rotational speed and the vertical axis represents the degree of vibration. Lines k 1  to k 3  are the same as those shown in  FIG. 10 . That is, line k 1  shows a profile which is extracted from interpolated three-dimensional data and which represents a relationship between the rotational speed and the frequency for any past cumulative number of rotations t 1 , and line k 2  shows a profile which is extracted from the interpolated three-dimensional data and which represents a relationship between the rotational speed and the frequency for any past cumulative number of rotations t 2 . A line k 3  represents an estimated profile representing a relationship between the rotational speed and the frequency for the current cumulative number of rotations tn (or the current point in time). 
     A dotted line k 4  represents an estimated profile representing a relationship between the rotational speed and the frequency for any future cumulative number of rotations (tn+Δt). Dotted line k 4  is estimated as follows. Initially, any reference rotational speed is set, and a profile representing the relationship between the cumulative number of rotations and the degree of vibration for the reference rotational speed is obtained from the interpolated three-dimensional data. The obtained profile (or function) is used to predict a degree of vibration for a future cumulative number of rotations (tn+Δt) at the reference rotational speed. Such a degree of vibration for the future cumulative number of rotations (tn+Δt) is predicted for a plurality of reference rotational speeds (Pa 1  to Pa 4  in  FIG. 12 ). 
     Then, the predicted degrees of vibration (Pa 1  to Pa 4 ) for the future cumulative number of rotations (tn+Δt) are used to fit dotted line k 4 . A functional form for dotted line k 4  is set with reference to lines k 1  to k 3 . Dotted line k 4  thus obtained represents a profile representing a relationship between the rotational speed and the frequency for the future cumulative number of rotations (tn+Δt), and for example allows a degree of vibration (a point P 2 ) for any rotational speed to be estimated for the future cumulative number of rotations (tn+Δt). 
       FIG. 13  is a flowchart of an example of a procedure of the predicted-value estimation process performed in step S 250  of  FIG. 9 . Referring to  FIG. 13  together with  FIG. 12 , estimation unit  150  sets any reference rotational speed (step S 410 ). The reference rotational speed is, for example, a rotational speed for point Pa 1  as has been described with reference to  FIG. 12 , and may be set by the operator of monitoring terminal  340  or may be automatically set by estimation unit  150  as desired. 
     Subsequently, estimation unit  150  obtains the data (the cumulative number of rotations and degree of vibration) for the reference rotational speed from the interpolated three-dimensional data calculated by data calculation unit  140 , and causes monitoring terminal  340  to display the data (step S 415 ). 
     Then, a functional form is set based on the data displayed in step S 415  (step S 420 ). The functional form is set by the operator of monitoring terminal  340 . Once the functional form has been set, estimation unit  150  uses the data obtained and displayed in step S 415  to fit the function set in step S 420  (step S 425 ). Fitting the function can be done for example through least squares. 
     Thereafter, estimation unit  150  causes monitoring terminal  340  to display the fitted function together with the data used to fit the function (step S 430 ). Subsequently, estimation unit  150  sets a cumulative number of rotations for prediction (tn+Δt) (step S 435 ). The cumulative number of rotations for prediction is set by the operator of monitoring terminal  340  and sets a future point in time for which providing a prediction is desired (i.e., a future cumulative number of rotations). 
     Then, estimation unit  150  uses the function fitted in step S 425  to calculate data indicating a degree of vibration for the cumulative number of rotations for prediction (tn+Δt) for predicted-value estimation, and causes monitoring terminal  340  to display the calculated data together with the function (step S 440 ). The data for predicted-value estimation, as calculated herein, is, for example, point Pa 1  shown in  FIG. 12 . 
     Subsequently, estimation unit  150  determines whether any data for predicted-value estimation has been determined (step S 445 ). It is necessary to generate data for predicted-value estimation for a plurality of points with the reference rotational speed varied, and a desired number of points is not obtained, it is determined that no data for predicted-value estimation is determined (NO in step S 445 ). In this case, the process returns to step S 410 , and further data for predicted-value estimation is generated with the reference rotational speed varied. 
     When it is determined in step S 445  that data for predicted-value estimation has been determined (YES in step S 445 ), a functional form is set based on a generated plurality of data for predicted-value estimation (e.g., Pa 1  to Pa 4  in  FIG. 12 ) (step S 450 ). The functional form is set by the operator of monitoring terminal  340 . Once the functional form has been set, estimation unit  150  uses the plurality of data for predicted-value estimation to fit a function for the cumulative number of rotations for prediction that is set in step S 435  (step S 455 ). In this case as well, for example, fitting the function can be done for example through least squares. 
     Then, estimation unit  150  causes monitoring terminal  340  to display the fitted function together with the data used to fit the function for predicted-value estimation (step S 460 ). Subsequently, estimation unit  150  obtains a rotational speed as a target for estimation (step S 465 ). The rotational speed as the target for estimation corresponds to the rotational speed for point P 2  that is set by the operator of monitoring terminal  340  and for which estimating a degree of vibration for the cumulative number of rotations for prediction (tn+Δt) is desired, as shown in  FIG. 12 . Estimation unit  150  uses the fitted function to estimate a degree of vibration for the rotational speed as the target for estimation (i.e., perform the predicted-value estimation) (step S 470 ). 
     Thus, according to the present embodiment, measurement data is not selected or excluded depending on an operating condition (or load) of wind power generation facility  10 , and any collected data (or data set) can be used to monitor a condition of wind power generation facility  10 . Thus, measurement data collected under various operating conditions can effectively be used to monitor conditions of a variety of types of apparatuses. 
     Further, according to the present embodiment, a cumulative load (or a number of rotations), rather than a data measurement time, can be used, and data for a period of time for which wind power generation facility  10  is stopped can be excluded. As a result, when data interpolation, function fitting, and the like are performed, discontinuity of data can be eliminated to increase accuracy. 
     Further, according to the present embodiment, displaying interpolated three-dimensional data on a three-dimensional graph allows a dangerous condition of wind power generation facility  10  to be confirmed together with an operating load of wind power generation facility  10 . 
     Further, in the present embodiment, generated interpolated three-dimensional data is used to estimate a relationship between an operating load (or a rotational speed) and measurement data (or a degree of vibration) for a current point in time (or a current cumulative number of rotations). Therefore, according to the present embodiment, the estimated relationship can be used to estimate measurement data for any operating load at the current point in time (i.e., to perform current-value estimation). 
     Further, in the present embodiment, generated interpolated three-dimensional data is used to predict a relationship between an operating load (a rotational speed) and measurement data (a degree of vibration) for any future point in time (or any future cumulative number of rotations). Therefore, according to the present embodiment, the predicted relationship can be used to estimate measurement data for any operating load at the future point in time (i.e., to perform predicted-value estimation). 
     In the above-described predicted-value estimation, a reference rotational speed may be set, and a future point in time (or a cumulative number of rotations) for which a degree of vibration exceeds a threshold value for that reference rotational speed may be predicted. 
       FIG. 14  is a flowchart of an example of a procedure of a process for predicting a future point in time (or a cumulative number of rotations) for which a degree of vibration exceeds a threshold value. A series of steps indicated in this flowchart is also performed in estimation unit  150 . 
     Referring to  FIG. 14 , estimation unit  150  sets a threshold value for the degree of vibration (step S 510 ). The threshold value can be appropriately determined or changed by the operator of monitoring terminal  340 , and is used for example to diagnose main shaft bearing  60  for abnormality. Further, estimation unit  150  sets a reference rotational speed (step S 515 ). The reference rotational speed is also set by the operator of monitoring terminal  340 , and a cumulative number of rotations when the degree of vibration exceeds the threshold value is predicted for the reference rotational speed. 
     Steps S 520  to S 535  are identical to the  FIG. 13  steps S 415  to S 430 , respectively. That is, estimation unit  150  obtains the data (the cumulative number of rotations and degree of vibration) for the reference rotational speed from interpolated three-dimensional data, and causes monitoring terminal  340  to display the data (step S 520 ). 
     Subsequently, a functional form is set (step S 525 ), and the data obtained and displayed in step S 520  is used to fit of the function set in step S 525 . Thereafter, estimation unit  150  causes monitoring terminal  340  to display the fitted function together with the data used to fit the function (step S 535 ). 
     Estimation unit  150  uses the fitted function to predict a cumulative number of rotations for which the degree of vibration set in step S 510  is attained (step S 540 ). Then, estimation unit  150  causes monitoring terminal  340  to display the predicted cumulative number of rotations together with the threshold value for the degree of vibration (step S 545 ). 
     Such a series of steps allows interpolated three-dimensional data generated from collected data to be used to predict when the degree of vibration exceeds the threshold value in the future, (i.e., to predict a cumulative number of rotations). 
     It should be understood that the embodiments disclosed herein are illustrative and not restrictive in any respect. The scope of the present invention is defined by the terms of the claims, rather than the embodiments described above, and is intended to include any modifications within the meaning and scope equivalent to the terms of the claims. 
     REFERENCE SIGNS LIST 
     
         
         
           
               10  wind power generation facility,  20  main shaft,  30  blade,  40  gearbox,  50  power generator,  60  main shaft bearing,  70 A- 70 H sensor,  80  measurement device,  90  nacelle,  100  tower,  120  data collection unit,  130  data storage unit,  140  data computation unit,  150  estimation unit,  320  Internet,  330  processing server,  340  monitoring terminal.