JP6824076B2 - Condition monitoring system and wind power generator - Google Patents

Description

The present invention relates to a condition monitoring system for monitoring the state of mechanical elements constituting the apparatus, and more particularly to a condition monitoring system for monitoring the condition of mechanical elements constituting the wind power generator.

In a wind power generator, power is generated by rotating a spindle connected to a blade that receives wind power, accelerating the rotation of the spindle by a speed increaser, and then rotating a rotor of a generator. Each of the spindle and the rotary shaft of the speed increaser and the generator is rotatably supported by rolling bearings, and a condition monitoring system (CMS) for diagnosing abnormalities in such bearings is known. .. In such a condition monitoring system, it is diagnosed whether or not the bearing is damaged by using the vibration waveform data measured by the vibration sensor fixed to the bearing.

For example, Japanese Patent Application Laid-Open No. 2009-20090 (Patent Document 1) discloses an abnormality diagnostic device for diagnosing abnormalities of rotating parts and sliding parts used in mechanical equipment. This abnormality diagnostic device performs frequency analysis of the signal generated from the rotating component to obtain the frequency component of the measured data, and from the frequency component of the measured data, the frequency corresponding to the abnormal frequency of vibration caused by the abnormality of the rotating component. Extract the ingredients. Then, the presence or absence of abnormality of the rotating component is diagnosed by comparing and collating the extracted frequency component with the threshold value.

In Patent Document 1, the above threshold values are individually set for each of the fundamental wave and the harmonic frequency of the abnormal frequency in order to make it less susceptible to the influence of ambient noise and improve the accuracy of diagnosis of the presence or absence of abnormality. ..

JP-A-2009-2009

In wind power generators, operating conditions change from moment to moment depending on the environment such as wind conditions that indicate how the wind blows. As the operating conditions change, the operating conditions such as vibration, spindle rotation speed, power generation amount, and wind speed change from moment to moment. For example, when the wind is strong, the load applied to the mechanical elements of the wind power generator is larger than when the wind is weak, so that the vibration becomes larger. In addition, since the load applied to the machine element changes depending on the wind direction, the vibration also changes.

As a result, in the condition monitoring system applied to the wind power generator, the vibration waveform data measured by the vibration sensor changes from moment to moment according to the change in the operating conditions of the wind power generator. In order to accurately diagnose the abnormality of the machine element based on the vibration waveform data, it is necessary to distinguish whether the change in vibration is due to a change in operating conditions or a damage to the machine element. For this purpose, it is important how to set the threshold value to be compared and collated 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 the presence or absence of abnormality in the mechanical elements constituting the wind power generation device in the state monitoring system and the wind power generation device provided with the state monitoring system. It is to provide a technique for setting a threshold for doing so.

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

According to the present invention, it is possible to appropriately set a threshold value for diagnosing the presence or absence of an abnormality in a mechanical element constituting a wind power generator, so that an accurate abnormality diagnosis can be realized.

It is a figure which showed roughly the structure of the wind power generation apparatus to which the condition monitoring system which concerns on embodiment of this invention is applied. It is a functional block diagram which functionally shows the structure of the data processing apparatus shown in FIG. It is a figure explaining the operation of the threshold value setting part. It is a graph which shows an example of the threshold value set by the threshold value setting part. It is a flowchart explaining the threshold setting process in the state monitoring system which concerns on embodiment. It is a flowchart explaining the control process 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. The same or corresponding parts in the drawings are designated 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 power generation device to which a condition monitoring system according to an embodiment of the present invention is applied. With reference to FIG. 1, the wind power generator 10 includes a spindle 20, a blade 30, a speed increaser 40, a generator 50, a spindle bearing (hereinafter, simply referred to as “bearing”) 60, and vibration. It includes a sensor 70 and a data processing device 80. The speed increaser 40, the generator 50, the bearing 60, the vibration sensor 70 and the data processing device 80 are housed in the nacelle 90, and the nacelle 90 is supported by the tower 100.

The spindle 20 enters the nacelle 90, is connected to the input shaft of the speed increaser 40, and is rotatably supported by the bearing 60. Then, the spindle 20 transmits the rotational torque generated by the blade 30 that has received the wind power to the input shaft of the speed increaser 40. The blade 30 is provided at the tip of the spindle 20, converts wind power into rotational torque, and transmits the wind power to the spindle 20.

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

The vibration sensor 70 is fixedly attached 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 composed of, for example, an acceleration sensor using a piezoelectric element.

The speed increaser 40 is provided between the spindle 20 and the generator 50, and increases the rotational speed of the spindle 20 to output to the generator 50. As an example, the speed increaser 40 is composed of a gear speed increase mechanism including a planetary gear, an intermediate shaft, a high speed shaft, and the like. Although not particularly shown, a plurality of bearings for rotatably supporting 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 electricity by the rotational torque received from the speed increaser 40. The generator 50 is composed of, for example, an induction generator. A bearing that rotatably supports the rotor is also provided in the generator 50.

The data processing device 80 is provided in the nacelle 90 and receives the vibration waveform data of the bearing 60 from the vibration sensor 70. The data processing device 80 diagnoses the abnormality of the bearing 60 by 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. With reference to FIG. 2, the 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 the diagnostic unit 150.

The BPF 110 receives the vibration waveform data of the bearing 60 from the vibration sensor 70. The BPF 110 executes 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 through the received vibration waveform data and blocks the low frequency component. The HPF is provided to remove the DC component included in the vibration waveform data of the bearing 60. If the output of the vibration sensor 70 does not include a DC component, the HPF may be omitted.

The BPF 110 may further include an envelope processing unit between the vibration sensor 70 and the HPF. When the envelope processing unit receives the 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 by the envelope processing unit. As an example, the vibration waveform data of the bearing 60 received from the vibration sensor 70 is rectified to an absolute value, and a low-pass filter (LPF) is used. (Low Pass Filter)), an envelope waveform of 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 the DC component contained in the envelope waveform and extract the AC component of the envelope waveform.

The BPF 110 may further contain an LPF. The LPF passes a signal component lower than a predetermined frequency through the received vibration waveform data and blocks the high frequency component.

The effective value calculation unit 120 receives the vibration waveform data of the filtered bearing 60 from the BPF 110. The effective value calculation unit 120 calculates the effective value of the vibration waveform data of the bearing 60 (also referred to as “RMS (Root Mean Square) value”), 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 without the envelope processing, for example, a part of the raceway ring of the bearing 60 is peeled off. However, the increase in vibration increases only when the rolling element passes through the peeled part. Although the increase in value is small for impulse-like vibration, it occurs when the surface of the contact part between the raceway ring and the rolling element is rough or poorly lubricated. The increase in value is large for continuous vibration.

On the other hand, as described above, when the envelope processing unit is provided, the effective value of the AC component of the envelope waveform calculated by the effective value calculation unit 120 is a continuous value that occurs when the surface of the raceway ring is rough or poor lubrication. The increase in value is small for vibration, and the increase in value is large for impulse-like vibration.

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

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

The diagnosis unit 150 diagnoses the abnormality of the bearing 60 based on the threshold value set by the threshold value setting unit 140. Specifically, the diagnostic unit 150 compares the read vibration waveform data with the threshold value set by the threshold value 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 measured vibration waveform data over time.

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 shown in FIG. 2, the threshold value 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 value calculation unit 146 (third calculation unit). And the threshold storage unit 148.

FIG. 3 is a diagram illustrating the operation of the threshold value setting unit 140. With reference to FIG. 3, the storage unit 130 receives the vibration waveform data of the bearing 60 from the 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 value setting unit 140 sequentially reads out the vibration waveform data for a specified period from the vibration waveform data stored in the storage unit 130, performs moving average calculation processing, standard deviation calculation processing, and threshold calculation processing, and performs threshold value calculation processing. Is calculated. It is desirable that the process of calculating this threshold value be performed when the vibration waveform data is sufficiently accumulated in the storage unit 130.

Specifically, the moving average calculation unit 142 calculates the moving average value of the latest vibration waveform data of the n bearings 60 read out. In the example of FIG. 3, when the latest vibration waveform data D1 to Dn of the n bearings 60 up to the time tn are read from the storage unit 130, the 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 the time tn + 1 are read from the storage unit 130, the 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 the time tn + 2 are read from the storage unit 130, the average value MAn + 2 of the n vibration waveform data D3 to Dn + 2 is calculated. As the moving average value, a simple moving average value or a weighted moving average value may be used. 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 value calculation unit 146 calculates the threshold value 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. Assuming that the threshold value is Th, the threshold value Th is expressed by the following equation (1).
Th = MA + k ・ σ… (1)
The coefficient k in the above equation (1) is a positive value (k> 0), and is set to, for example, k = 2. That is, the threshold Th is set to, for example, "MA + 2σ".

In the example of FIG. 3, the threshold value Thn is calculated using the moving average value MAn and the standard deviation σ at the time tun, the threshold value Thn + 1 is calculated using the moving average value MAn + 1 and the standard deviation σ at the time tn + 1, and the movement at the time tn + 2 The threshold Thn + 2 is calculated using the mean 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 Th contains a seasonal variation component. Therefore, the diagnosis unit 150 is configured to use the threshold value Th at the same time as the current season among the threshold values Th stored in the threshold value storage unit 148. That is, the diagnostic unit 150 is configured to use the threshold value Th set based on the past vibration waveform data under the same operating conditions as the vibration waveform data.

The operating conditions of wind power generators generally depend on the environment such as wind conditions, and therefore change mainly depending on the season. With the above configuration, the diagnosis unit 150 diagnoses the abnormality of the bearing 60 by using the threshold value Th corresponding to the change of the operating conditions such as seasonal factors. As shown in FIG. 3, since the threshold value Th is set by performing statistical processing on n vibration waveform data, it changes according to a change in the operating conditions of the wind power generation device 10. That is, the threshold Th can reflect the change in the operating conditions of the wind power generation device 10.

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

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

Black circles in FIG. 4 indicate effective values of vibration waveform data of the bearing 60. The broken line in FIG. 4 indicates the temporal change of the moving average value MA of the effective values of the n 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 by using the moving average value MA and the standard deviation σ of the effective values of the n vibration waveform data. The threshold value is calculated using the equation Th = MA + 2σ.

As shown in FIG. 4, the effective value of the vibration waveform data changes significantly 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 also changes significantly due to the operating conditions that change with the seasons.

In order to make an accurate diagnosis even in a period when 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, in this way, the effective value exceeding the threshold value frequently occurs during the period when the effective value becomes relatively large, and accurate diagnosis becomes difficult.

In the present embodiment, the threshold value is set based on the moving average value calculated from the effective value of the vibration waveform data of the bearing 60 recorded in the past fixed period. In particular, the threshold value is set using the effective value of the vibration waveform data at the time when the past bearing 60 is judged to be normal. Therefore, if the abnormality is diagnosed based on the threshold value set for the same operating conditions in the past (for example, the same period in the past), the threshold value is relatively small during the period when 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. In this way, since the threshold value reflects the change in the operating conditions of the wind power generation device 10, accurate abnormality diagnosis can be realized.

FIG. 5 is a flowchart illustrating a threshold value setting process in the condition 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.

With reference to FIG. 5, the data processing apparatus 80 specifies a range (period) of data used for threshold calculation in step S01. The range of data may be automatically set, for example, the same month one year ago, or may be configured to be externally given as a parameter by communication or the like.

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

In step S03, the threshold value setting unit 140 determines whether or not 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 (when NO is determined in S03), the subsequent processes S04 to S07 are skipped.

On the other hand, when the number of effective value data of the read vibration waveform data is n or more (when YES is determined in S03), the threshold setting unit 140 proceeds to step S04 and proceeds to step S04 to read the latest n pieces. Calculate the moving average value of the effective value of the vibration waveform data.

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

In step S08, the threshold value setting unit 140 determines whether or not the processing of all the vibration waveform data in the target period has been completed. If the process is not completed (at the time of NO determination in S08), the process is returned to S02.

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

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

Next, when the vibration waveform data of the filtered bearing 60 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 out the threshold value set based on the effective value of the past vibration waveform data under the same operating conditions 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 diagnostic unit 150 reads out the threshold values set at the same time in the past as the current season.

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 in step S16. When the effective value is equal to or less than the threshold value (when NO is determined in S16), the diagnosis unit 150 diagnoses that the bearing 60 is normal and skips the subsequent processing 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 on the bearing 60, which is one of the mechanical elements constituting the wind power generation device 10, to diagnose the abnormality of the bearing 60. The mechanical element is not limited to the bearing 60. For example, the vibration sensor is installed in the bearing 60 provided in the speed increaser 40 or the generator 50 together with the bearing 60 or in place of the bearing 60, and the speed increaser 40 is carried out by the same method as in each of the above embodiments. It is possible to diagnose an abnormality of the bearing provided inside or inside the generator 50.

Further, in the above embodiment, the data processing device 80 corresponds to an embodiment of the "processing device" in the present invention, and the threshold setting unit 140 and the diagnostic unit 150 correspond to the "setting unit" and the "setting unit" in the present invention, respectively. Corresponds to one embodiment of the "diagnosis unit". Further, 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 the "first calculation unit" and the "second calculation unit" in the present invention, respectively. , Corresponds to one embodiment of the "third arithmetic unit" and the "storage unit".

The embodiments disclosed this time should be considered as exemplary in all respects and not restrictive. The scope of the present invention is shown by the scope of claims rather than the description of the embodiment described above, and is intended to include all modifications within the meaning and scope equivalent to the scope of claims.

10 Wind power generator, 20 spindle, 30 blades, 40 speed increaser, 50 generator, 60 bearings, 70 vibration sensor, 80 data processing device, 90 nacelle, 100 tower, 110 HPF, 120 effective value calculation unit, 130 storage unit , 140 Threshold setting unit, 142 Moving average calculation unit, 144 Standard deviation calculation unit, 146 Threshold calculation unit, 148 Threshold storage unit, 150 Diagnostic unit.

Claims (4)

A condition monitoring system that monitors the status of machine elements that make up a device.
A vibration sensor for measuring the vibration waveform of the machine element and
It is equipped with a processing device for diagnosing an abnormality of the mechanical element.
The processing device
A diagnostic unit that diagnoses an abnormality of the machine element by comparing the effective value of the vibration waveform data output from the vibration sensor with the threshold value, and
Including a setting unit for setting the threshold value
The setting unit
A first calculation unit that calculates a moving average value of n effective values of the vibration waveform data output from the vibration sensor (n is an integer of 2 or more).
A second calculation unit that calculates the standard deviation of the effective values of the n vibration waveform data, and
A state monitoring system including the moving average value calculated by the first calculation unit and a third calculation unit that calculates the threshold value based on 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 past effective value of the vibration waveform data whose operating conditions are the same as the effective value of the vibration waveform data output from the vibration sensor is read out.
The condition monitoring according to claim 1 or 2, wherein an abnormality of the machine element is diagnosed by comparing the threshold value read from the storage unit with the effective value of the vibration waveform data output from the vibration sensor. system. A wind power generator comprising the condition monitoring system according to any one of claims 1 to 3.

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