A method and device for monitoring fan blade faults and a fan

Abstract

The present invention provides a method, device and fan for monitoring fan blade faults, the method comprising: obtaining the vibration signal of each fan blade in each sampling period; respectively calculating the impact information sensitivity value corresponding to each vibration signal; determining the information phase difference based on the impact information sensitivity value of each vibration signal; and determining the blade state of the fan according to the information phase difference. The fan blade fault monitoring method, device and fan provided by the present invention obtain the impact information sensitivity value by analyzing the index of the vibration signal of each blade that is sensitive to the impact information, and then use the information phase difference obtained by comparison as the basis for identifying the blade state of the fan according to the comparison of the impact information sensitivity values ​​of each blade, and can effectively identify the blade fault that causes the vibration signal to generate the impact information, and there is no need to establish a fault database, the monitoring result is not easily affected by bad points, it is easy to implement, and the monitoring cost is low.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbines, and in particular to a method and device for monitoring blade failures of a wind turbine, and a wind turbine. Background Art

[0002] Blades are one of the most critical components of wind turbines. They operate in harsh natural environments at high altitudes for a long time and are subjected to random wind loads. Therefore, studying their fault diagnosis technology to ensure the normal operation of blades is of great significance to improving the safe and economical operation of the units.

[0003] At present, the following methods are mainly used for online diagnosis of blade faults: strain monitoring method, noise monitoring method and vibration monitoring method. Among them, the strain monitoring method is to judge blade faults by monitoring the strain of the blade; the noise monitoring method is to obtain the spectrum characteristics of each blade noise signal by monitoring the noise signal of the blade, and then judge the blade fault according to the spectrum characteristics of each blade noise signal; the vibration monitoring method is to obtain the vibration information of each blade and the natural frequency of the blade by monitoring the vibration signal of the blade to judge the blade fault.

[0004] The strain monitoring method has high hardware requirements and high monitoring costs; the noise monitoring method requires the establishment of a database of normal samples and fault samples, which requires a large amount of calculation and has low accuracy; the vibration monitoring method mainly judges blade failures by judging the similarity of the vibration waveforms of each blade or by the difference in the blade's natural frequency, and the monitoring accuracy is easily affected by the operating conditions of the monitoring equipment. Summary of the invention

[0005] The present invention provides a fan blade fault monitoring method, device and fan, which are used to solve the related defects of conventional blade fault monitoring methods, such as strict requirements on monitoring conditions, the need to establish a fault feature library, large amount of calculation, low detection efficiency and poor detection accuracy.

[0006] In a first aspect, the present invention provides a method for monitoring wind turbine blade faults, comprising: obtaining vibration signals of each blade of the wind turbine in each sampling period; respectively calculating the impact information sensitivity value corresponding to each vibration signal; determining the information phase difference based on the impact information sensitivity value of each vibration signal; and determining the blade status of the wind turbine according to the information phase difference.

[0007] According to a fan blade fault monitoring method provided by the present invention, the impact information sensitive value is a kurtosis value or a maximum peak value.

[0008] According to a wind turbine blade fault monitoring method provided by the present invention, when the impact information sensitivity value is a kurtosis value, the impact information sensitivity value corresponding to each vibration signal is calculated separately, including: performing high-pass filtering on each vibration signal to obtain the corresponding filtered vibration signal; calculating the fourth-order center distance corresponding to each filtered vibration signal as the kurtosis value.

[0009] A method for monitoring faults of a fan blade provided by the present invention, determining the information difference degree based on the impact information sensitivity values of the vibration signals, includes: using the fourth-order central moment of the standard deviation of each vibration signal to normalize each kurtosis value respectively to obtain the kurtosis factor corresponding to each kurtosis value; determining the information difference degree, where the information difference degree is the variance of all kurtosis factors.

[0010] A method for monitoring faults of a fan blade provided by the present invention, in the case where the impact information sensitivity value is the maximum peak value, determining the information difference degree based on the impact information sensitivity values of the vibration signals, includes: using the root mean square value of each vibration signal to normalize the maximum peak value respectively to obtain the peak factor corresponding to each maximum peak value; determining the information difference degree, where the information difference degree is the variance of all peak factors.

[0011] A method for monitoring faults of a fan blade provided by the present invention, in the case where the impact information sensitivity value is the maximum peak value, determining the information difference degree based on the impact information sensitivity values of the vibration signals, includes: using the absolute average value of each vibration signal to normalize the maximum peak value respectively to obtain the impulse factor corresponding to each maximum peak value; determining the information difference degree, where the information difference degree is the variance of all impulse factors.

[0012] A method for monitoring faults of a fan blade provided by the present invention, determining the blade state of the fan according to the information difference degree, includes: obtaining the information difference degrees corresponding to the vibration signals of each blade of the fan within continuous K sampling periods; in the case of determining that M information difference degrees are greater than a first preset threshold, determining that the blade state of the fan is a serious fault, where the second preset threshold ≤ M ≤ K; in the case of determining that N information difference degrees are greater than the first preset threshold, determining that the blade state of the fan is a general fault, where the third preset threshold ≤ N < M; in the case of determining that L information difference degrees are greater than the first preset threshold, determining that the blade state of the fan is normal, where L is less than the third preset threshold.

[0013] A method for monitoring faults of a fan blade provided by the present invention, after determining the blade state of the fan, further includes: if it is determined that the blade state of the fan is a serious fault, controlling the fan to stop; if it is determined that the blade state of the fan is a general fault, generating an alarm reminder; if it is determined that the blade state of the fan is normal, maintaining the operation of the fan.

[0014] A method for monitoring faults of a fan blade provided by the present invention, the vibration signal includes a first vibration signal perpendicular to the blade direction and a second vibration signal parallel to the blade and along the blade width direction.

[0015] According to a method for monitoring fan blade faults provided by the present invention, determining the blade state of the fan according to the information phase difference includes: determining the first blade state according to the information phase difference corresponding to the first vibration signal, and determining the second blade state according to the information phase difference corresponding to the second vibration signal; if at least one of the first blade state and the second blade state is a serious fault, determining that the blade state of the fan is a serious fault; if both the first blade state and the second blade state are non-serious faults, but at least one is a general fault, determining that the blade state of the fan is a general fault; if both the first blade state and the second blade state are non-serious faults and non-general faults, determining that the blade state of the fan is normal.

[0016] In the second aspect, the present invention also provides a wind turbine blade fault monitoring device, comprising: a signal acquisition unit, used to obtain the vibration signal of each blade of the wind turbine in each sampling period; a first operation unit, used to respectively calculate the impact information sensitivity value corresponding to each vibration signal; a second operation unit, used to determine the information phase difference based on the impact information sensitivity value of each vibration signal; a state judgment unit, used to determine the blade state of the wind turbine according to the information phase difference.

[0017] In a third aspect, the present invention provides a wind turbine, comprising a wind turbine body, in which a blade monitoring processor and a plurality of biaxial acceleration sensors are provided, each of the biaxial acceleration sensors being respectively provided on each blade of the wind turbine body for acquiring a vibration signal of each blade; the wind turbine body also comprises a memory and a program or instruction stored in the memory and executable on the blade monitoring processor, wherein when the program or instruction is executed by the blade monitoring processor, the steps of any of the above-mentioned wind turbine blade fault monitoring methods are performed.

[0018] In a fourth aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any of the above-described methods for monitoring wind blade faults are implemented.

[0019] In a fifth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described methods for monitoring wind blade faults.

[0020] In a sixth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for monitoring wind turbine blade faults.

[0021] The fan blade fault monitoring method, device and fan provided by the present invention obtain impact information sensitivity values ​​by analyzing the indicators of sensitivity to impact information related to the vibration signal of each blade, and then use the information phase difference obtained by comparison as the basis for identifying the blade state of the fan based on the comparison of the impact information sensitivity values ​​of each blade. The blade fault that causes the vibration signal to generate impact information can be effectively identified without the need to establish a fault database. The monitoring results are not easily affected by bad points, are easy to implement, and have low monitoring costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a flow chart of the fan blade fault monitoring method provided by the present invention;

[0024] Figure 2 It is a schematic diagram of a flow chart of calculating information difference provided by the present invention;

[0025] Figure 3 It is a comparison diagram of each vibration signal before and after high-pass filtering in the time domain provided by the present invention;

[0026] Figure 4 It is a schematic diagram of the trend of information difference provided by the present invention;

[0027] Figure 5 It is a schematic diagram of a process for determining a blade state according to a phase difference provided by the present invention;

[0028] Figure 6 It is a structural schematic diagram of a fan blade fault monitoring device provided by the present invention;

[0029] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] It should be noted that in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. The orientation or position relationship indicated by the terms "upper", "lower" and the like is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be a connection between two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0032] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0033] Combine the following Figure 1-Figure 6 A fan blade fault monitoring method and device provided by an embodiment of the present invention are described.

[0034] Figure 1 FIG. 1 is a flow chart of a fan blade fault monitoring method provided by the present invention, such as Figure 1 As shown, including but not limited to the following steps:

[0035] Step 101: Obtain the vibration signal of each blade of the fan in each sampling period.

[0036] Optionally, the present invention can pre-set a sensor for collecting vibration signals, such as an acceleration sensor, on each blade of the wind turbine to be tested. The vibration signal of each blade obtained can be vibration acceleration waveform data fitted in the time domain after collecting the vibration acceleration value of the blade in a certain direction within a sampling period according to a preset sampling frequency.

[0037] Generally speaking, each fan has three blades. If only the vibration signal of each blade in one direction (such as perpendicular to the blade direction) is measured, a total of three sets of vibration signals corresponding to each blade can be obtained in one sampling period.

[0038] If the vibration signal of each blade is measured in the direction parallel to the blade and along the width of the blade on the basis of the above, then there will be two groups of vibration signals corresponding to each blade in one sampling period, and a total of six groups of vibration signals will be obtained for each wind turbine in one sampling period.

[0039] It should be noted that, in general, the principle of the fan blade fault monitoring method provided by the present invention is to compare the vibration signals of the three blades of each fan in the same direction to monitor the blade status according to the comparison results. Therefore, when the present invention simultaneously collects the vibration signals of each blade in multiple directions, it processes the vibration signals in a certain direction as a group of independent ones to obtain the blade status judgment result in that direction. Finally, the blade status judgment results determined in each direction can be combined to determine the final blade status of the fan.

[0040] Step 102: Calculate the impact information sensitivity value corresponding to each vibration signal. The amplitude distribution of the vibration signal can often reflect whether the blade is normal or not. Generally speaking, the more stable the amplitude of the vibration signal is, the more normal the state of the blade is, and the greater the fluctuation of the amplitude of the vibration signal is, the more abnormal its state is.

[0041] The present invention analyzes the waveform data corresponding to the vibration signal of each blade to obtain the value corresponding to the indicator sensitive to the impact information, that is, obtains the impact information sensitivity value corresponding to each vibration signal, in order to analyze the blade fault that causes the vibration signal to generate impact information through the impact information sensitivity value.

[0042] Step 103: Determine the information phase difference based on the impact information sensitivity value of each vibration signal.

[0043] For example, the average difference between the three shock information sensitivity values ​​may be calculated, or the variance of the three shock information sensitivity values ​​may be calculated as the information difference, which is not specifically limited in the present invention.

[0044] The present invention mainly calculates the impact information sensitivity values ​​corresponding to the vibration signals of the three blades of the same fan in the same direction respectively, and then characterizes the difference between the three impact information sensitivity values ​​by the information phase difference. It can be understood that: the information phase difference is used to characterize the difference of the vibration signals related to the three blades of the same fan.

[0045] Step 104: Determine the blade status of the wind turbine according to the information difference.

[0046] Since the information phase difference can effectively characterize the difference in the vibration signals of the three blades, and since the vibration signals of the three blades are theoretically very similar when the fan is operating normally, even if there are large differences in the moment when they are subject to abnormal interference, they will quickly recover after the abnormal interference is removed.

[0047] Therefore, the present invention calculates the information phase difference through the vibration signals of the three blades, and uses the information phase difference as a basis for judging the blade state of the fan. Generally speaking, the larger the information phase difference, especially when the information phase difference calculated in multiple consecutive sampling periods exceeds the theoretical value, it can be considered that the blade state of the fan is a fault state.

[0048] The method for monitoring fan blade faults provided by the present invention obtains an impact information sensitivity value by analyzing an index sensitive to impact information related to the vibration signal of each blade, and then uses the information phase difference obtained by comparison as a basis for identifying the blade state of the fan based on the comparison of the impact information sensitivity values ​​of each blade. The method can effectively identify blade faults that cause vibration signals to generate impact information, and there is no need to establish a fault database. The monitoring results are not easily affected by bad points, are easy to implement, and have low monitoring costs.

[0049] Based on the content of the above embodiment, as an optional embodiment, the impact information sensitive value is a kurtosis value or a maximum peak value.

[0050] The kurtosis value is a numerical statistic that reflects the distribution characteristics of random variables and is particularly sensitive to impact signals. When the fan is operating normally, due to the influence of various uncertain factors, the amplitude distribution of the vibration signal is close to the normal distribution. If a blade failure occurs, the probability density of large amplitudes in the vibration signal increases, the distribution of the signal amplitude will deviate from the normal distribution, the normal curve will be skewed or dispersed, and the kurtosis value will increase accordingly. Therefore, the larger the absolute value of the kurtosis value, the more serious the fault is, indicating that the fan blade has deviated from its normal state.

[0051] Figure 2 is a schematic diagram of a flow chart of calculating information difference provided by the present invention, such as Figure 2As shown in FIG. 1 , when the wind turbine to be tested includes three blades, the vibration signals of blade 1, blade 2 and blade 3 in each sampling period are obtained respectively. After high-pass filtering of each vibration signal, the kurtosis value 1 related to blade 1, the kurtosis value 2 related to blade 2 and the kurtosis value 3 related to blade 3 are obtained. Then, each kurtosis value is normalized to obtain the kurtosis factor 1 related to blade 1, the kurtosis factor 2 related to blade 2 and the kurtosis factor 3 related to blade 3. Finally, the variance between the kurtosis factor 1, the kurtosis factor 2 and the kurtosis factor 3 is calculated as the information difference ( Figure 2 In , the kurtosis difference is expressed as the information difference A).

[0052] The maximum peak value of the vibration signal can intuitively indicate the vibration condition of the blade when it is impacted, so the maximum peak value of the vibration signal can also reflect the blade status of the fan.

[0053] Taking the kurtosis value as an example, when a blade of the wind turbine has a specific fault (such as cracking or delamination), the vibration signal will show high-frequency impact characteristics and the kurtosis value will increase. At this time, the difference between the kurtosis values ​​of the three blades will increase, that is, the information phase difference (also called the kurtosis phase difference value) will increase. Therefore, the blade status can be monitored through the information phase difference.

[0054] By the same token, when a specific fault occurs in a blade of the fan, the maximum value of the blade vibration signal will also increase. At this time, the difference between the maximum peak values ​​of the three blades will also increase, that is, the information phase difference will increase. Therefore, the maximum peak value can also be used to calculate the information phase difference, thereby realizing the monitoring of the blade status.

[0055] The fan blade fault monitoring method provided by the present invention diagnoses whether the blade has faults by selecting indicators such as kurtosis value and maximum peak value that are sensitive to impact information, and can effectively identify blade faults that cause vibration signals to generate impact information.

[0056] Based on the contents of the above embodiments, as an optional embodiment, when the impact information sensitivity value is a kurtosis value, respectively calculating the impact information sensitivity value corresponding to each vibration signal includes: respectively performing high-pass filtering on each vibration signal to obtain the corresponding filtered vibration signal; calculating the fourth-order center distance corresponding to each filtered vibration signal as the kurtosis value.

[0057] The high-pass filter provided by the present invention is a filtering method, the rule of which is that high-frequency signals higher than a set critical value can pass normally, while low-frequency signals lower than the set critical value are blocked and weakened.

[0058] Figure 3 is a schematic diagram of a flow chart of calculating information difference provided by the present invention, such as Figure 3As shown, assuming that the fan to be monitored is numbered "F014", high-pass filtering is performed on the vibration signals of its three blades in the direction perpendicular to the blades. It can be intuitively found that blade No. 1 of fan No. F014 may be faulty.

[0059] pass Figure 3 By comparing the upper and lower parts in the figure, it can be determined that after the vibration signal is processed by high-pass filtering, the unnecessary low-frequency components and low-frequency interference in the vibration signal are effectively removed, making the characteristics that can reflect the fault of the fan blade more prominent.

[0060] After high-pass filtering is performed on the vibration signal corresponding to each blade to obtain the corresponding filtered vibration signal, the fourth-order center distance of the normal distribution related to each filtered vibration signal is calculated respectively as the kurtosis value of each filtered vibration signal.

[0061] Among them, the calculation formula of the fourth-order center distance can be:

[0062]

[0063]

[0064] Among them, β is the fourth-order center distance; N is the total number of samples; x(i) is the i-th sample variable; is the average value.

[0065] The fan blade fault monitoring method provided by the present invention uses the fourth-order central moment of the discrete signal as the kurtosis value, which reflects the convexity of the peak of the signal probability density function, making the kurtosis value very sensitive to large-amplitude vibration, which is very helpful for detecting pulse information in the vibration signal. When the probability of large-amplitude vibration increases, the kurtosis value of the vibration signal will increase rapidly.

[0066] Based on the content of the above embodiment, as an optional embodiment, the above-mentioned information difference is determined based on the impact information sensitivity value of each vibration signal, mainly including: using the fourth-order center distance of the standard deviation of each vibration signal to normalize each kurtosis value respectively, and obtain the kurtosis factor corresponding to each kurtosis value; determine the information difference, and the information difference is the variance of all kurtosis factors.

[0067] Since the kurtosis value is a dimensional index, although it is sensitive to signal characteristics, it will also change due to changes in working conditions (such as load) and is easily affected by environmental interference, and has the defect of being unstable. In contrast, the dimensionless index can eliminate the influence of these disturbance factors. In view of this, the present invention normalizes the kurtosis value corresponding to each blade, converting it from a dimensional index to a dimensionless index. The specific calculation formula can be:

[0068]

[0069] Among them, β is the kurtosis value (i.e. the fourth-order center distance), is the standard deviation of the fourth-order center distance, and K is the calculated kurtosis factor.

[0070] Figure 4 It is a schematic diagram of the trend of information difference provided by the present invention, such as Figure 4 As shown in FIG. 1 , the variance of the kurtosis factor related to the three blades on the same wind turbine to be tested (i.e., the information difference at this time can be understood as the kurtosis difference) can characterize the difference between the high-frequency impact characteristics of the three blades. Generally speaking, when the three blades of the wind turbine are operating normally, the variance of the kurtosis factor approaches 0.

[0071] Among them, the variance σ 2 The calculation formula can be:

[0072]

[0073] Among them, x i is the variable; μ is the population mean; N is the number of cases in the population, which can be 3 in this case.

[0074] It should be noted that, after calculating the kurtosis factors related to the three blades on the same wind turbine to be tested, the present invention further calculates the variance between the kurtosis value factors related to the three blades to judge the state of the wind turbine. The smaller the variance, the smaller the probability of a blade failure in the wind turbine. At this time, it is not ruled out that two or three blades fail at the same time. However, since the probability of multiple blades failing at the same time is small, and there must be differences in the causes of the simultaneous failures, the severity of the failures, etc., the variance between the kurtosis value factors will also increase. Therefore, the method for monitoring the failure of the wind turbine blades by calculating the information difference (i.e., the variance of the kurtosis factor) provided by the present invention is used. It only needs to compare the vibration signals of each blade, and there is no need to establish a fault database to achieve monitoring. The monitoring results are not easily affected by bad points, which is easy to implement and has low monitoring costs.

[0075] Based on the content of the above embodiment, as an optional embodiment, when the impact information sensitivity value is the maximum peak, the above impact information sensitivity value based on each vibration signal determines the information difference, which mainly includes: using the root mean square value of each vibration signal to normalize the maximum peak respectively, and obtain the peak factor corresponding to each maximum peak; determining the information difference, which is the variance of all peak factors.

[0076] Optionally, in the wind blade fault monitoring method provided by the present invention, the impact information sensitivity value for blade fault feature identification may not be limited to the kurtosis value, but may also be other indicators that are sensitive to impact information. For example, the peak factor similarity value may be further determined based on the maximum peak value, so as to judge whether the wind blade is faulty or not based on the size of the peak factor similarity value.

[0077] The peak factor is the value obtained by determining the maximum peak value of the vibration signal and dividing it by the root mean square value of the vibration signal. It is a statistical indicator used to detect whether there is impact in the signal. Its calculation formula is:

[0078]

[0079]

[0080] Among them, C f is the peak factor; X max is the maximum peak value; X rms is the root mean square value.

[0081] The maximum peak value refers to the maximum value of the amplitude.

[0082] Furthermore, after the peak factors of the three leaves are obtained, the variances corresponding to the three peak factors may be calculated as the information difference.

[0083] Based on the content of the above embodiment, as an optional embodiment, when the impact information sensitivity value is the maximum peak, the above-mentioned impact information sensitivity value based on each vibration signal determines the information phase difference, and can also include: using the absolute average value of each vibration signal to normalize the maximum peak respectively, and obtain the pulse factor corresponding to each maximum peak; determine the information phase difference, and the information phase difference is the variance of all pulse factors.

[0084] The pulse factor is a normalized value obtained by calculating the maximum peak value of the vibration signal and dividing it by the absolute average value of the vibration signal.

[0085] The difference between the impulse factor and the peak factor lies in the normalization process. Since the absolute average value of the same set of data is less than the effective value, the impulse factor will be greater than the peak factor. The impulse factor can also be used to detect whether there is impact in the vibration signal.

[0086] Among them, the pulse factor I f The calculation formula can be:

[0087]

[0088]

[0089] It should be noted that the information phase difference proposed in the present invention can be obtained by calculating the variance between the three blade kurtosis factors, peak factors or pulse factors calculated by periodic sampling, or it can be determined based on one or more of the margin factors, waveform factors, skewness factors, skewness factors, etc. between the three blades and determined by variance calculation.

[0090] Among them, the margin factor CL f The maximum peak value of the vibration signal is calculated, and then the root amplitude of the vibration signal X is used r The normalized value can effectively characterize the wear of the fan blades. The calculation formula is:

[0091]

[0092] Form Factor W s It is the value obtained by calculating the effective value of the vibration signal and then normalizing it with the absolute average value of the vibration signal. It can also be regarded as: waveform factor W s = Pulse factor / peak factor. Calculation formula:

[0093]

[0094] The skewness factor describes the asymmetry of the vibration signal. If there is friction or collision in a certain direction, it will cause the waveform of the vibration signal to be asymmetric, making the skewness factor C w Increase, the calculation formula can be:

[0095]

[0096] In general, the physical meanings of peak factor, pulse factor and margin factor are similar. Both peak factor and pulse factor are indicators used to detect whether there is impact in the signal, and margin factor is used to detect the wear condition of fan blades. Kurtosis factor is also a response to the impact characteristics of vibration signal.

[0097] When evaluating the performance of the above-mentioned time domain characteristic value indicators, sensitivity and stability are usually used to measure. Pulse index, peak index and kurtosis are all sensitive to impact-type faults, especially when the fault occurs in the early stage, they have a significant increase; but after rising to a certain extent, as the fault gradually develops, they will decrease, indicating that they are highly sensitive to early faults, but the stability is not good. The stability of the effective value is good, but it is not sensitive to early fault signals. In order to achieve better fault diagnosis results, they can be combined and applied at the same time to achieve multi-angle monitoring of fan blade faults.

[0098] Figure 5 FIG. 1 is a flow chart of determining the state of a blade according to the phase difference provided by the present invention, such as Figure 5 As shown, after the method provided in the above embodiment is used to calculate the information phase difference of the wind turbine blade to be tested in each sampling period (assuming that a total of K sampling periods are performed), the information phase difference (dimensionless data) calculated in each sampling period can be compared with the first preset threshold value.

[0099] If condition 1 is met, that is, among the K information phase differences calculated within K sampling periods (assuming K=100), M (assuming M=20) are greater than the first preset threshold, the vibration signal of the fan blade is abnormal within M sampling periods.

[0100] It can be assumed that the second threshold is 8. Since M is greater than the second threshold and less than the number of sampling cycles K, the relevant wind turbine blade may be in serious failure.

[0101] If condition 1 is satisfied, that is, among the K information phase differences calculated in K sampling periods, there are N (it can be assumed that the second preset threshold value = 5) information phase differences detected in the sampling periods are greater than the first preset threshold value, that is, the vibration signals of the fan blades in a total of 5 sampling periods are abnormal,

[0102] It can be further assumed that the third preset threshold is 0. Since N is greater than the third preset threshold and N is less than , it is considered that the relevant wind turbine blade is in a general fault.

[0103] Furthermore, when it is determined that L (it can be assumed that L=1) of the information phase differences are greater than the first preset threshold, since L is less than the information phase difference greater than the first preset threshold during a general fault in the sampling period, it can be considered that the blades of the wind turbine to be tested are stable and fault-free as a whole.

[0104] The fan blade fault monitoring method provided by the present invention collects the vibration signals of each blade of the fan to be tested within multiple sampling cycles, and performs online calculation to obtain the information phase difference related to the vibration signals of each blade. When the continuous and / or multiple information phase differences are greater than the first preset threshold, it is considered that the fan blade has a fault. At the same time, the present invention can also accurately distinguish between general faults and serious faults according to the proportion of the number of sampling cycles for obtaining abnormal vibration signals to the number of all sampling cycles. The calculation amount is small, and there is no need to establish a fault database. The monitoring results are not easily affected by bad points, so it can realize real-time online monitoring of fan blade faults.

[0105] Based on the content of the above embodiment, as an optional embodiment, after determining the blade state of the wind turbine, the method further includes:

[0106] If it is determined that the blade state of the fan is a serious fault, the fan is controlled to stop;

[0107] If it is determined that the blade state of the fan is a general fault, an alarm reminder is generated;

[0108] If it is determined that the blade status of the fan is normal, the fan is kept running.

[0109] Combination Figure 5 As shown, when it is determined that a serious fault has occurred in the blades of the fan, a fault shutdown instruction can be sent to the fan controller to shut down the fan to avoid casualties of personnel and equipment caused by the blade failure.

[0110] When it is determined that a general fault has occurred in the blade, the main control alarm is triggered to remind the staff to pay more attention and analyze the authenticity of the fault to avoid further deterioration of the fault.

[0111] In addition, the normal operation of the fan can be maintained and continuously monitored according to the pre-set sampling cycle.

[0112] The fan blade fault monitoring method provided by the present invention can timely formulate corresponding fault response strategies according to different monitoring results of the fan blades, and can ensure the maximum efficiency of the fan on the basis of safe operation of the fan.

[0113] Based on the content of the above embodiment, as an optional embodiment, the vibration signal includes a first vibration signal perpendicular to the blade direction and a second vibration signal parallel to the blade and along the blade width direction.

[0114] Optionally, the present invention can collect a first vibration signal perpendicular to the blade direction (i.e., the blade flapping direction) and a second vibration signal parallel to the blade and along the blade width direction (i.e., the blade swing direction) through a dual-axis acceleration sensor pre-installed on each blade, and respectively sample the methods provided in the above embodiments to process the first vibration signal and / or second vibration signal of each blade to generate an information phase difference corresponding to the first vibration signal and an information phase difference corresponding to the second vibration signal.

[0115] Furthermore, the above-mentioned determination of the blade status of the fan according to the information difference may include but is not limited to the following steps:

[0116] Determine a first blade state according to a phase difference of information corresponding to the first vibration signal, and determine a second blade state according to a phase difference of information corresponding to the second vibration signal;

[0117] If at least one of the first blade state and the second blade state is a serious fault, determining that the blade state of the fan is a serious fault;

[0118] If both the first blade state and the second blade state are non-serious faults, but at least one is a general fault, determining that the blade state of the fan is a general fault;

[0119] If both the first blade state and the second blade state are non-serious faults and non-general faults, it is determined that the blade state of the fan is normal.

[0120] Specifically, since the fan blade fault monitoring method provided by the present invention can simultaneously collect the vibration signals of each blade in two different directions, the vibration signals in each vibration direction can be processed independently, that is:

[0121] First, according to the first vibration signal of each blade in the direction perpendicular to the blade, the corresponding information phase difference is comprehensively calculated, and according to the comparison between the information phase difference and the first preset threshold, the blade state of the fan is determined and recorded as the first blade state.

[0122] Then, according to the first vibration signal of each blade in the direction perpendicular to the blade, the corresponding information phase difference is comprehensively calculated, and according to the comparison between the information phase difference and the first preset threshold, the blade state of the blower is determined and recorded as the second blade state.

[0123] Table 1 The actual blade status of the fan

[0124]

[0125] Table 1 is a table for determining the actual blade state of the fan provided by the present invention. The actual blade state of the fan is determined by combining the first blade state and the second blade state as shown in Table 1.

[0126] Among them, the blade conditions of the fan are from good to bad: normal, general fault to serious fault.

[0127] The above determination of the fan blade state only refers to determining the actual fan blade state based on a certain information phase difference in two vibration directions. In the actual monitoring process, different information phase differences can also be extracted from the same vibration signal of each blade, such as: calculating the information phase difference (recorded as kurtosis phase difference, peak phase difference, pulse phase difference) based on the kurtosis factor, peak factor and pulse factor respectively; determining the corresponding blade state in two directions based on different information phase differences; finally, determining the fan blade state by combining all the determined blade states. Considering the safe operation of the fan, the worst blade state determined by any information phase difference in any direction is taken as the fan blade state.

[0128] Figure 6 Schematic diagram of the structure of the fan blade fault monitoring device provided by the present invention. Figure 6As shown, it mainly includes a signal acquisition unit 111, a first operation unit 112, a second operation unit 113 and a state judgment unit 114, wherein:

[0129] The signal acquisition unit 111 is mainly used to obtain the vibration signal of each blade of the fan in each sampling period; the first operation unit 112 is mainly used to calculate the impact information sensitivity value corresponding to each vibration signal respectively; the second operation unit 113 is mainly used to determine the information difference based on the impact information sensitivity value of each vibration signal; the state judgment unit 114 is mainly used to determine the blade state of the fan according to the information difference.

[0130] It should be noted that the fan blade fault monitoring device provided in the embodiment of the present invention can perform the fan blade fault monitoring described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0131] The wind turbine blade fault monitoring device provided by the present invention obtains an impact information sensitivity value by analyzing an index sensitive to impact information related to the vibration signal of each blade, and then uses the information phase difference obtained by comparison as a basis for identifying the blade state of the wind turbine based on the comparison of the impact information sensitivity values ​​of each blade. It can effectively identify the blade fault that causes the vibration signal to generate impact information, and there is no need to establish a fault database. The monitoring result is not easily affected by bad points, is easy to implement, and has low monitoring cost.

[0132] Based on the contents of the above embodiments, as an optional embodiment, the present invention also provides a wind turbine, mainly comprising a wind turbine body, in which a blade monitoring processor and a plurality of biaxial acceleration sensors are arranged, each biaxial acceleration sensor is respectively arranged on each blade of the wind turbine body, for obtaining the vibration signal of each blade; and also comprises a memory and a program or instruction stored in the memory and executable on the blade monitoring processor, wherein when the program or instruction is executed by the blade monitoring processor, the steps of the wind turbine blade fault monitoring method provided in any of the above embodiments are executed.

[0133] The wind turbine provided by the present invention can obtain an impact information sensitivity value by analyzing an index sensitive to impact information related to the vibration signal of each blade, and then, based on a comparison of the impact information sensitivity values ​​of each blade, use the information phase difference obtained by the comparison as a basis for identifying the blade state of the wind turbine. This can effectively identify blade faults that cause vibration signals to generate impact information, and there is no need to establish a fault database. The monitoring results are not easily affected by bad points, are easy to implement, and have low monitoring costs.

[0134] Figure 7 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the fan blade fault monitoring method, which includes: obtaining the vibration signal of each fan blade in each sampling period; respectively calculating the impact information sensitivity value corresponding to each vibration signal; determining the information difference based on the impact information sensitivity value of each vibration signal; and determining the blade state of the fan according to the information difference.

[0135] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0136] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the wind turbine blade fault monitoring method provided by the above methods, and the method includes: obtaining the vibration signal of each blade of the wind turbine in each sampling period; respectively calculating the impact information sensitivity value corresponding to each vibration signal; based on the impact information sensitivity value of each vibration signal, determining the information phase difference; and determining the blade status of the wind turbine according to the information phase difference.

[0137] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the wind turbine blade fault monitoring method provided by the above-mentioned embodiments, the method comprising: obtaining the vibration signal of each blade of the wind turbine in each sampling period; respectively calculating the impact information sensitivity value corresponding to each vibration signal; determining the information phase difference based on the impact information sensitivity value of each vibration signal; and determining the blade status of the wind turbine according to the information phase difference.

[0138] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0139] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring fan blade faults, characterized in that: include: Obtain the vibration signal of each blade of the fan in each sampling period; Calculate the impact information sensitivity value corresponding to each vibration signal respectively, wherein the impact information sensitivity value is the kurtosis value or the maximum peak value; Determining the information phase difference based on the impact information sensitivity value of each vibration signal; Determining the blade status of the fan according to the information difference; In the case where the shock information sensitivity value is a kurtosis value, respectively calculating the shock information sensitivity value corresponding to each vibration signal includes: Perform high-pass filtering on each vibration signal to obtain a corresponding filtered vibration signal; Calculate the fourth-order center distance corresponding to each filtered vibration signal as the kurtosis value; The step of determining the information phase difference based on the impact information sensitivity value of each vibration signal includes: Using the fourth-order center distance of the standard deviation of each vibration signal, each kurtosis value is normalized to obtain the kurtosis factor corresponding to each kurtosis value; Determining the information difference, where the information difference is the variance of all kurtosis factors; Determining the blade status of the fan according to the information difference includes: Obtaining the information phase difference corresponding to the vibration signal of each blade of the fan within K consecutive sampling periods; When it is determined that the difference between the M pieces of information is greater than a first preset threshold, it is determined that the blade state of the wind turbine is a serious fault, and the second preset threshold ≤M≤K; When it is determined that the difference between the N pieces of information is greater than the first preset threshold, it is determined that the blade state of the fan is a general fault, and the third preset threshold ≤ N <M; When it is determined that the phase differences of the L pieces of information are greater than the first preset threshold, it is determined that the blade state of the wind turbine is normal, and L is less than the third preset threshold.

2. The fan blade fault monitoring method according to claim 1, characterized in that: In a case where the shock information sensitivity value is the maximum peak value, determining the information phase difference based on the shock information sensitivity value of each vibration signal includes: Using the root mean square value of each vibration signal, normalizing the maximum peak value to obtain the peak factor corresponding to each maximum peak value; The information difference is determined, where the information difference is the variance of all peak factors.

3. The fan blade fault monitoring method according to claim 1, characterized in that: In a case where the shock information sensitivity value is the maximum peak value, determining the information phase difference based on the shock information sensitivity value of each vibration signal includes: Using the absolute average value of each vibration signal, normalizing the maximum peak values ​​respectively to obtain the pulse factor corresponding to each maximum peak value; The information phase difference is determined, where the information phase difference is the variance of all pulse factors.

4. The fan blade fault monitoring method according to claim 1, characterized in that: The vibration signal includes a first vibration signal perpendicular to the blade direction and a second vibration signal parallel to the blade and along the blade width direction.

5. The fan blade fault monitoring method according to claim 4, characterized in that: Determining the blade status of the fan according to the information difference includes: Determine a first blade state according to a phase difference of information corresponding to the first vibration signal, and determine a second blade state according to a phase difference of information corresponding to the second vibration signal; If at least one of the first blade state and the second blade state is a serious fault, determining that the blade state of the fan is a serious fault; If both the first blade state and the second blade state are non-serious faults, but at least one is a general fault, determining that the blade state of the fan is a general fault; If both the first blade state and the second blade state are non-serious faults and non-general faults, it is determined that the blade state of the fan is normal.

6. A fan blade fault monitoring device, characterized in that: include: A signal acquisition unit, used to obtain the vibration signal of each blade of the fan in each sampling period; A first computing unit, used to calculate the impact information sensitivity value corresponding to each vibration signal, wherein the impact information sensitivity value is a kurtosis value or a maximum peak value; In the case where the shock information sensitivity value is a kurtosis value, respectively calculating the shock information sensitivity value corresponding to each vibration signal includes: Perform high-pass filtering on each vibration signal to obtain a corresponding filtered vibration signal; Calculate the fourth-order center distance corresponding to each filtered vibration signal as the kurtosis value; A second computing unit, configured to determine an information phase difference based on the impact information sensitivity value of each vibration signal; The step of determining the information phase difference based on the impact information sensitivity value of each vibration signal includes: Using the fourth-order center distance of the standard deviation of each vibration signal, each kurtosis value is normalized to obtain the kurtosis factor corresponding to each kurtosis value; Determining the information difference, where the information difference is the variance of all kurtosis factors; A state judgment unit, used to determine the state of the blades of the fan according to the information difference, includes: Obtaining the information phase difference corresponding to the vibration signal of each blade of the fan within K consecutive sampling periods; When it is determined that the difference between the M pieces of information is greater than a first preset threshold, it is determined that the blade state of the wind turbine is a serious fault, and the second preset threshold ≤M≤K; When it is determined that the difference between the N pieces of information is greater than the first preset threshold, it is determined that the blade state of the fan is a general fault, and the third preset threshold ≤ N <M; When it is determined that the phase differences of the L pieces of information are greater than the first preset threshold, it is determined that the blade state of the wind turbine is normal, and L is less than the third preset threshold.

7. A fan, characterized in that: It comprises a fan body, in which a blade monitoring processor and a plurality of biaxial acceleration sensors are arranged, each of which is arranged on each blade of the fan body, and is used to obtain a vibration signal of each blade; It also includes a memory and a program or instruction stored in the memory and executable on the blade monitoring processor, wherein when the program or instruction is executed by the blade monitoring processor, the steps of the wind turbine blade fault monitoring method as described in any one of claims 1 to 5 are executed.

Images