JP2017181500A - State monitoring system and wind power generator - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technique that sets a threshold for diagnosing presence or absence of abnormality of mechanical elements consisting of a wind power generator in a state monitoring system and the wind power generator including the state monitoring system.SOLUTION: A state monitoring system comprises: an oscillation sensor 70 for measuring an oscillation waveform of a bearing of a wind power generator; and a data processing device 80. In the data processing device 80, a diagnosis unit 150 is configured to compare an effective value of oscillation waveform data to be output from the oscillation sensor 70 with a threshold to thereby diagnose abnormality of mechanical elements. A threshold setting unit 140 is configured to set the threshold, and includes: a first computation unit that computes a moving average value of n pieces of oscillation waveform data (n is an integer equal to or more than 2) to be output from the oscillation sensor; a second computation unit that computes a standard deviation of the n pieces of the oscillation waveform data; a third computation unit that computes a threshold on the basis of the moving average value computed by the first computation unit and the standard deviation computed by the second computation unit; and a threshold storage unit 140 that stores the computed threshold.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a state monitoring system that monitors the state of machine elements that constitute a device, and more particularly, to a state monitoring system that monitors the state of machine elements that constitute a wind turbine generator.

  In a wind turbine generator, power is generated by rotating a main shaft connected to a blade that receives wind power, rotating the main shaft with a speed increaser, and then rotating a rotor of the power generator. Each of the main shaft, the speed increaser, and the rotating shaft of the generator is rotatably supported by a rolling bearing, and a condition monitoring system (CMS: Condition Monitoring System) for diagnosing such a bearing abnormality is known. . In such a state monitoring system, whether or not the bearing is damaged is diagnosed using vibration waveform data measured by a vibration sensor fixed to the bearing.

  For example, Japanese Patent Laid-Open No. 2009-20090 (Patent Document 1) discloses an abnormality diagnosis device that diagnoses abnormality of rotating parts and sliding parts used in mechanical equipment. This abnormality diagnosis device performs frequency analysis of signals generated from rotating parts to obtain frequency components of actual measurement data, and from the frequency components of the actual measurement data, a frequency corresponding to an abnormal frequency of vibration caused by abnormality of the rotating parts. Extract ingredients. Then, the presence / absence of an abnormality of the rotating component is diagnosed by comparing and collating the extracted frequency component with a threshold value.

  In Patent Document 1, the threshold is individually set for each of the fundamental wave and the harmonic frequency of the abnormal frequency in order to reduce the influence of ambient noise and improve the accuracy of the diagnosis of the presence or absence of abnormality. .

JP 2009-20090 A

  In the wind turbine generator, the operating conditions change from moment to moment depending on the environment such as the wind condition indicating how the wind blows. As the operating conditions change, operating conditions such as vibration, spindle rotation speed, power generation amount, and wind speed also change from moment to moment. For example, when the wind is strong, the load applied to the mechanical elements of the wind turbine generator is larger than when the wind is weak, and therefore the vibration is increased. Moreover, since the load applied to the machine element also changes depending on the wind direction, the vibration also changes.

  As a result, in the state monitoring system applied to the wind turbine generator, the vibration waveform data measured by the vibration sensor changes from moment to moment according to changes in the operating conditions of the wind turbine generator. In order to accurately diagnose an abnormality in a machine element based on vibration waveform data, it is necessary to distinguish whether the change in vibration is due to a change in operating conditions or damage to the machine element. For this purpose, it is important how to set a threshold value to be compared with the vibration waveform data in order to diagnose the presence or absence of abnormality.

  Therefore, the present invention has been made to solve such a problem, and an object of the present invention is to diagnose whether there is an abnormality in the mechanical elements constituting the wind power generation device in the state monitoring system and the wind power generation device including the state monitoring system. It is to provide a technique for setting a threshold value for doing so.

  The state monitoring system according to the present invention is a state monitoring system for monitoring the state of machine elements constituting the apparatus. The state monitoring system includes a vibration sensor for measuring a vibration waveform of a machine element, and a processing device for diagnosing an abnormality of the machine element. The processing device includes a diagnosis unit and a setting unit. The diagnosis unit is configured to diagnose an abnormality of the machine element by comparing the vibration waveform data output from the vibration sensor with a threshold value. The setting unit is configured to set a threshold value. The setting unit includes a first calculation unit that calculates a moving average value of n (n is an integer of 2 or more) vibration waveform data output from the vibration sensor, and a first calculation unit that calculates a standard deviation of the n vibration waveform data. 2 calculation units, and a third calculation unit that calculates a threshold based on the moving average value calculated by the first calculation unit and the standard deviation calculated by the second calculation unit.

  According to this invention, since the threshold value for diagnosing the presence / absence of abnormality of the mechanical elements constituting the wind turbine generator can be appropriately set, accurate abnormality diagnosis can be realized.

It is the figure which showed roughly the structure of the wind power generator to which the state monitoring system which concerns on embodiment of this invention was applied. It is a functional block diagram which shows the structure of the data processor shown in FIG. 1 functionally. It is a figure explaining operation | movement of a threshold value setting part. It is a graph which shows an example of the threshold set by a threshold setting part. It is a flowchart explaining the setting process of the threshold value in the state monitoring system which concerns on embodiment. It is a flowchart explaining the control processing for diagnosing the abnormality of the bearing 60 in the state monitoring system which concerns on embodiment.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are denoted by the same reference numerals, and the description thereof will not be repeated.

  FIG. 1 is a diagram schematically showing a configuration of a wind turbine generator to which a state monitoring system according to an embodiment of the present invention is applied. Referring to FIG. 1, a wind turbine generator 10 includes a main shaft 20, a blade 30, a speed increaser 40, a generator 50, a main shaft bearing (hereinafter simply referred to as “bearing”) 60, and vibration. A sensor 70 and a data processing device 80 are provided. The step-up gear 40, the generator 50, the bearing 60, the vibration sensor 70, and the data processing device 80 are stored in the nacelle 90, and the nacelle 90 is supported by the tower 100.

  The main shaft 20 enters the nacelle 90 and is connected to the input shaft of the speed increaser 40 and is rotatably supported by the bearing 60. The main shaft 20 transmits the rotational torque generated by the blade 30 receiving the wind force to the input shaft of the speed increaser 40. The blade 30 is provided at the tip of the main shaft 20 and converts wind force into rotational torque and transmits it to the main shaft 20.

  The bearing 60 is fixed in the nacelle 90 and rotatably supports the main shaft 20. The bearing 60 is composed of a rolling bearing, and is composed of, for example, a self-aligning roller bearing, a tapered roller bearing, a cylindrical roller bearing, or a ball bearing. These bearings may be single row or double row.

  The vibration sensor 70 is fixed to the bearing 60. The vibration sensor 70 measures the vibration waveform of the bearing 60 and outputs the measured vibration waveform data to the data processing device 80. The vibration sensor 70 is constituted by, for example, an acceleration sensor using a piezoelectric element.

  The speed increaser 40 is provided between the main shaft 20 and the generator 50, and increases the rotational speed of the main shaft 20 to output to the generator 50. As an example, the speed increaser 40 is configured by a gear speed increasing mechanism including a planetary gear, an intermediate shaft, a high speed shaft, and the like. In addition, although not specifically illustrated, a plurality of bearings that rotatably support a plurality of shafts are also provided in the speed increaser 40.

  The generator 50 is connected to the output shaft of the speed increaser 40, and generates power by the rotational torque received from the speed increaser 40. The generator 50 is constituted by, for example, an induction generator. A bearing for rotatably supporting the rotor is also provided in the generator 50.

  The data processing device 80 is provided in the nacelle 90 and receives vibration waveform data of the bearing 60 from the vibration sensor 70. The data processing device 80 diagnoses the abnormality of the bearing 60 using the vibration waveform data of the bearing 60 according to a preset program.

  FIG. 2 is a functional block diagram functionally showing the configuration of the data processing device 80 shown in FIG. Referring to FIG. 2, data processing device 80 includes a band pass filter (hereinafter referred to as “BPF (Band Pass Filter)”) 110, an effective value calculation unit 120, a storage unit 130, and a threshold setting unit 140. And a diagnostic unit 150.

  The BPF 110 receives vibration waveform data of the bearing 60 from the vibration sensor 70. The BPF 110 performs a filtering process on the vibration waveform data of the bearing 60. The BPF 110 is, for example, a high pass filter (HPF (High Pass Filter)). The HPF passes a signal component higher than a predetermined frequency with respect to the received vibration waveform data, and blocks a low frequency component. The HPF is provided to remove a direct current component included in the vibration waveform data of the bearing 60. Note that the HPF may be omitted if the output of the vibration sensor 70 does not include a DC component.

  The BPF 110 may further include an envelope processing unit between the vibration sensor 70 and the HPF. When the envelope processing unit receives vibration waveform data of the bearing 60 from the vibration sensor 70, the envelope processing unit performs envelope processing on the received vibration waveform data to generate an envelope waveform of the vibration waveform data of the bearing 60. Various known methods can be applied to the envelope processing calculated in the envelope processing unit. As an example, the vibration waveform data of the bearing 60 received from the vibration sensor 70 is rectified into an absolute value, and a low-pass filter (LPF) is obtained. By passing (Low Pass Filter), an envelope waveform of the vibration waveform data of the bearing 60 is generated. In this case, when the HPF receives the envelope waveform of the vibration waveform data of the bearing 60 from the envelope processing unit, the HPF passes a signal component higher than a predetermined frequency for the received envelope waveform, and blocks the low frequency component. . That is, the HPF is configured to remove a direct current component included in the envelope waveform and extract an alternating current component of the envelope waveform.

  The BPF 110 may further include an LPF. The LPF passes a signal component lower than a predetermined frequency with respect to the received vibration waveform data, and blocks the high frequency component.

  The effective value calculation unit 120 receives vibration waveform data of the bearing 60 subjected to the filter processing from the BPF 110. The effective value calculation unit 120 calculates an effective value (also referred to as “RMS (Root Mean Square) value)) of the vibration waveform data of the bearing 60, and stores the calculated effective value of the vibration waveform data in the storage unit 130. Output to.

  Since the effective value of the vibration waveform of the bearing 60 calculated by the effective value calculation unit 120 is the effective value of the raw vibration waveform that has not been subjected to the envelope processing, for example, separation occurs in a part of the bearing ring of the bearing 60. However, although the increase in the value is small for the impulse-like vibration in which the vibration increases only when the rolling element passes through the peeled portion, it occurs when the surface of the contact portion between the race ring and the rolling element is rough or poorly lubricated. For continuous vibrations, the value increases.

  On the other hand, as described above, when the envelope processing unit is provided, the effective value of the alternating current component of the envelope waveform calculated by the effective value calculating unit 120 is the continuous value generated when the raceway surface is rough or poorly lubricated. The increase in value is small for vibration, and the increase is large for impulse vibration.

  The storage unit 130 stores the effective value of the vibration waveform data of the bearing 60 calculated by the effective value calculating unit 120 every moment. The storage unit 130 is configured by, for example, a readable / writable nonvolatile memory. The storage unit 130 is configured to store effective values of vibration waveform data of at least the latest n bearings (n is an integer of 2 or more). In the following description, the effective value of the vibration waveform data is simply referred to as “vibration waveform data”.

  The threshold setting unit 140 sets a threshold used for diagnosing the presence / absence of an abnormality in the bearing 60. The threshold setting unit 140 outputs the set threshold to the diagnosis unit 150. Details of the threshold setting in the threshold setting unit 140 will be described later.

  The diagnosis unit 150 diagnoses the abnormality of the bearing 60 based on the threshold set by the threshold setting unit 140. Specifically, the diagnosis unit 150 compares the read vibration waveform data with the threshold set by the threshold setting unit 140. When it is determined that the vibration waveform data exceeds the threshold value, the diagnosis unit 150 issues an alarm for notifying the abnormality of the bearing 60. Further, the diagnosis unit 150 diagnoses the abnormality of the bearing 60 based on the transition of the temporal change of the measured vibration waveform data.

  Next, the threshold value setting process executed by the threshold value setting unit 140 will be described with reference to FIGS. 2 and 3.

  As illustrated in FIG. 2, the threshold setting unit 140 includes a moving average calculation unit 142 (first calculation unit), a standard deviation calculation unit 144 (second calculation unit), and a threshold calculation unit 146 (third calculation unit). And a threshold storage unit 148.

  FIG. 3 is a diagram for explaining the operation of the threshold setting unit 140. Referring to FIG. 3, storage unit 130 receives vibration waveform data of bearing 60 from effective value calculation unit 120 at predetermined time intervals. D1 to Dn + 2 in FIG. 3 represent vibration waveform data given to the storage unit 130 at predetermined time intervals.

  The threshold setting unit 140 sequentially reads vibration waveform data for a specified period from the vibration waveform data stored in the storage unit 130, performs a moving average calculation process, a standard deviation calculation process, and a threshold calculation process. Is calculated. The processing for calculating the threshold value is desirably performed when vibration waveform data is sufficiently accumulated in the storage unit 130.

  Specifically, the moving average calculator 142 calculates a moving average value of the latest read vibration waveform data of the n bearings 60. In the example of FIG. 3, when the latest vibration waveform data D1 to Dn of the n bearings 60 up to time tn are read from the storage unit 130, an average value MAn of the n vibration waveform data D1 to Dn is calculated. . Next, when the latest vibration waveform data D2 to Dn + 2 of the n bearings 60 up to time tn + 1 are read from the storage unit 130, an average value MAn + 1 of the n vibration waveform data D2 to Dn + 1 is calculated. Further, when the latest vibration waveform data D3 to Dn + 2 of the n bearings 60 up to time tn + 2 are read from the storage unit 130, an average value MAn + 2 of the n vibration waveform data D3 to Dn + 2 is calculated. The moving average value may be a simple moving average value or a weighted moving average value. In this way, the moving average calculation unit 142 calculates the moving average value MA using the latest vibration waveform data of the n bearings 60.

The standard deviation calculation unit 144 calculates the standard deviation σ of the vibration waveform data of the n bearings 60.
The threshold calculation unit 146 calculates a threshold using the moving average value MA calculated by the moving average calculation unit 142 and the standard deviation σ calculated by the standard deviation calculation unit 144. When the threshold is Th, the threshold Th is expressed by the following equation (1).
Th = MA + k · σ (1)
The coefficient k in the above formula (1) is a positive value (k> 0), and is set to k = 2, for example. That is, the threshold Th is set to “MA + 2σ”, for example.

  In the example of FIG. 3, threshold Thn is calculated using moving average value MAn and standard deviation σ at time tn, threshold Thn + 1 is calculated using moving average value MAn + 1 and standard deviation σ at time tn + 1, and moving at time tn + 2. The threshold value Thn + 2 is calculated using the average value MAn + 2 and the standard deviation σ. In this way, the threshold value calculation unit 146 calculates the threshold value Th, and stores the calculated threshold value Th in the threshold value storage unit 148.

  The threshold value Th includes a seasonal variation component. Therefore, the diagnosis unit 150 is configured to use a threshold value Th that is the same as the current season among the threshold values Th stored in the threshold value storage unit 148. That is, the diagnosis unit 150 is configured to use a threshold value Th set based on past vibration waveform data under the same operating conditions as the vibration waveform data.

  Since the operating conditions of a wind turbine generator generally depend on the environment such as the wind conditions, the operating conditions vary mainly depending on the season. With the above-described configuration, the diagnosis unit 150 performs the diagnosis of the abnormality of the bearing 60 using the threshold Th corresponding to a change in operating conditions such as seasonal factors. As shown in FIG. 3, the threshold value Th is set by performing statistical processing on n pieces of vibration waveform data, and thus changes according to a change in operating conditions of the wind turbine generator 10. That is, the threshold value Th can reflect a change in operating conditions of the wind turbine generator 10.

  Then, by comparing the vibration waveform data of the bearing 60 that changes according to the operating conditions of the wind power generator 10 and the threshold value Th that reflects the operating conditions, the comparison result shows that the operating conditions of the wind power generator 10 change. The influence can be reduced. As a result, it is possible to capture a change in vibration caused by damage to the bearing 60, so that an accurate abnormality diagnosis can be realized.

  FIG. 4 is a graph illustrating an example of the threshold set by the threshold setting unit 140. FIG. 4 shows the effective value of the vibration waveform data measured by the vibration sensor 70 in the measurement period of about one year from February 1 of a certain year to February 26 of the following year, and the moving average of the vibration waveform data. The time change with a value and a threshold is shown.

  Black circles in FIG. 4 indicate effective values of vibration waveform data of the bearing 60. The broken line in FIG. 4 shows the temporal change of the moving average value MA of the effective values of the n pieces of vibration waveform data. In the example of FIG. 4, n = 120. The solid line in FIG. 4 shows the temporal change of the threshold value set using the moving average value MA and the standard deviation σ of the effective values of the n pieces of vibration waveform data. The threshold value is calculated using the formula Th = MA + 2σ.

  As shown in FIG. 4, the effective value of the vibration waveform data changes greatly during the measurement period. In the example of FIG. 4, the effective value is relatively large from December to January, while the effective value is relatively small from July to August. In this way, the effective value changes greatly due to the operating conditions that change from season to season.

  In order to perform an accurate diagnosis even in a period in which the effective value is relatively small, it is conceivable to set the threshold value to a constant value based on the effective value in the period. However, if this is done, an effective value that exceeds the threshold frequently occurs during a period in which the effective value is relatively large, and accurate diagnosis becomes difficult.

  In the present embodiment, the threshold is set based on the moving average value calculated from the effective value of the vibration waveform data of the bearing 60 recorded during a past fixed period. In particular, the threshold is set using the effective value of the vibration waveform data at the time when it is determined that the past bearing 60 is normal. Therefore, if the abnormality is diagnosed based on the threshold value set for the same past operating conditions (for example, the same period in the past), the threshold value is relatively low during the period in which the effective value is relatively small. On the other hand, the threshold value becomes relatively large during the period when the effective value becomes relatively large. Thus, since the threshold value reflects the change in the operating condition of the wind turbine generator 10, an accurate abnormality diagnosis can be realized.

  FIG. 5 is a flowchart for explaining threshold setting processing in the state monitoring system according to the embodiment. The setting process shown in FIG. 5 is executed by the data processing device 80 (FIG. 2) by designating a data range (period) used for threshold calculation.

  Referring to FIG. 5, in step S01, the data processing device 80 designates a data range (period) used for threshold calculation. The data range may be automatically set, for example, the same month one year ago, or may be configured to be given by communication or the like as a parameter from the outside.

  Next, in step S02, the vibration waveform data stored in the storage unit 130 are sequentially read from the head of the designated data range.

  In step S03, the threshold setting unit 140 determines whether the number of effective values of the read vibration waveform data is n or more. When the number of effective values of the read vibration waveform data is less than n (NO in S03), the subsequent processes S04 to S07 are skipped.

  On the other hand, when the number of effective values of the read vibration waveform data is n or more (at the time of YES determination in S03), the threshold setting unit 140 proceeds to step S04, and the latest n pieces of read-out vibration waveform data are read. Calculates the moving average value of the effective value of vibration waveform data.

  Subsequently, in step S05, the threshold setting unit 140 calculates the standard deviation of the effective values of the latest read n pieces of vibration waveform data. Then, in step S06, the threshold setting unit 140 sets a threshold using the moving average value calculated in step S04 and the standard deviation calculated in step S05. In step S07, the threshold setting unit 140 sequentially stores the set thresholds in the threshold storage unit 148 (FIG. 2). The threshold value is stored together with the time information of the data used for the calculation, and is used for selecting the threshold value used for the diagnosis.

  In step S08, the threshold setting unit 140 determines whether the processing has been completed for all vibration waveform data in the target period. If the process has not been completed (NO in S08), the process returns to S02.

  FIG. 6 is a flowchart for explaining a control process for diagnosing an abnormality of the bearing 60 in the state monitoring system according to the embodiment. The control processing shown in FIG. 6 is repeatedly executed by the data processing device 80 at predetermined time intervals.

  Referring to FIG. 6, data processing device 80 receives vibration waveform data of bearing 60 from vibration sensor 70 in step S11. In the data processing device 80, the BPF 110 executes a filtering process on the vibration waveform data of the bearing 60 in step S12.

  Next, when the vibration waveform data of the bearing 60 subjected to the filtering process is received from the BPF 110 in step S13, the effective value calculation unit 120 calculates the effective value of the vibration waveform data of the bearing 60. The storage unit 130 stores the effective value of the vibration waveform data calculated by the effective value calculation unit 120 in step S14.

  In step S15, the diagnosis unit 150 reads the threshold value set based on the effective value of the past vibration waveform data under the same operating condition as the effective value of the vibration waveform data from the threshold value storage unit 148 in the threshold value setting unit 140. . For example, the diagnosis unit 150 reads out the threshold values set for the current season and the past simultaneous period.

  In step S16, the data processing device 80 compares the effective value of the vibration waveform data calculated in step S13 with the threshold value read in step S15. When the effective value is less than or equal to the threshold value (NO determination in S16), the diagnosis unit 150 diagnoses that the bearing 60 is normal and skips the subsequent process S17. On the other hand, when the effective value is larger than the threshold value (when YES is determined in S16), the diagnosis unit 150 diagnoses that the bearing 60 is abnormal in step S17 and issues an alarm.

  In the above embodiment, the vibration sensor 70 is installed in the bearing 60 that is one of the mechanical elements constituting the wind power generator 10 to diagnose the abnormality of the bearing 60. It will be described in a definite manner that the mechanical element is not limited to the bearing 60. For example, a vibration sensor is installed in a bearing provided in the speed increaser 40 or in the generator 50 together with or in place of the bearing 60, and the speed increaser 40 is obtained by the same method as in each of the above embodiments. An abnormality of a bearing provided in the generator 50 or in the generator 50 can be diagnosed.

  In the above embodiment, the data processing device 80 corresponds to an example of the “processing device” in the present invention, and the threshold setting unit 140 and the diagnosis unit 150 are the “setting unit” and the “setting unit” in the present invention, respectively. This corresponds to an example of the “diagnostic unit”. In the above embodiment, the moving average calculation unit 142, the standard deviation calculation unit 144, the threshold value calculation unit 146, and the threshold value storage unit 148 are respectively the “first calculation unit” and the “second calculation unit” in the present invention. , Corresponding to one example of the “third arithmetic unit” and the “storage unit”.

  The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is shown not by the above description of the embodiments but by the scope of claims for patent, and is intended to include meanings equivalent to the scope of claims for patent and all modifications within the scope.

  DESCRIPTION OF SYMBOLS 10 Wind power generator, 20 Main shaft, 30 Blade, 40 Booster, 50 Generator, 60 Bearing, 70 Vibration sensor, 80 Data processing device, 90 Nacelle, 100 Tower, 110 HPF, 120 RMS value calculation part, 130 Storage part 140 threshold setting unit, 142 moving average calculation unit, 144 standard deviation calculation unit, 146 threshold calculation unit, 148 threshold storage unit, 150 diagnosis unit.

Claims (4)

A state monitoring system for monitoring the state of machine elements constituting the apparatus,
A vibration sensor for measuring a vibration waveform of the machine element;
A processing device for diagnosing an abnormality of the machine element,
The processor is
A diagnostic unit for diagnosing an abnormality of the machine element by comparing the effective value of the vibration waveform data output from the vibration sensor with a threshold value;
A setting unit for setting the threshold,
The setting unit
A first calculation unit that calculates a moving average value of effective values of n pieces of vibration waveform data (n is an integer of 2 or more) output from the vibration sensor;
A second calculator that calculates a standard deviation of effective values of the n pieces of vibration waveform data;
A state monitoring system comprising: a third calculation unit that calculates the threshold value based on the moving average value calculated by the first calculation unit and the standard deviation calculated by the second calculation unit.   The state monitoring system according to claim 1, wherein the third calculation unit calculates the threshold value by adding a value obtained by multiplying the standard deviation by a predetermined coefficient to the moving average value. The setting unit further includes a storage unit that stores the threshold value calculated by the third calculation unit,
The diagnostic unit
From the storage unit, the threshold value calculated based on the effective value of the past vibration waveform data that has the same operating conditions as the effective value of the vibration waveform data output from the vibration sensor is read,
The state monitoring according to claim 1 or 2, wherein the abnormality of the mechanical element is diagnosed by comparing the threshold value read from the storage unit with an effective value of the vibration waveform data output from the vibration sensor. system.   A wind turbine generator comprising the state monitoring system according to any one of claims 1 to 3.

Images