CN115712860A - Fan blade vibration alarm method and device - Google Patents
Fan blade vibration alarm method and device Download PDFInfo
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Abstract
The invention provides a fan blade vibration alarm method, which comprises the following steps: acquiring a frequency domain curve of fan blade vibration, an alarm actual measurement value and an alarm threshold value of a natural frequency amplitude index; training a pre-constructed hybrid model according to a frequency domain curve of fan blade vibration, an alarm actual measurement value of a natural frequency amplitude index and an alarm threshold value to obtain a trained hybrid model; and acquiring an actually measured frequency domain signal of the wind turbine generator, and inputting the actually measured frequency domain signal of the wind turbine generator into the trained hybrid model to obtain a vibration alarm result of the blade of the wind turbine generator. Based on the method, the capture of the generator fault of the wind turbine generator and the identification of different fault types are realized, so that the times of false alarm can be reduced.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a fan blade vibration alarm method and device.
Background
The fan blade belongs to a key constituent part in the wind turbine generator and plays a role in energy conversion. In the running process of the fan, the safety and the reliability of the blades must be ensured, the conversion efficiency of absorbing wind energy can be effectively improved, and the beneficial influence is brought to the whole wind turbine set. The stable operation of the fan can be ensured by improving the performance of the blade. However, the working environment of the blade is complex, and the blade is subject to the effects of centrifugal force, aerodynamic force, thermal stress, bending stress and the like, and even has the phenomena of rain, snow, frost erosion, lightning accumulation, damage and the like. Based on the method, the vibration of the fan blade is considered to be detected and analyzed, the fatigue degree of the fan blade is accurately and quickly judged, and the method is an effective means for maintaining the safe operation of the fan and improving the wind energy utilization rate.
The existing unit is different from the actual running condition of a wind field after a fan is assembled based on wind tunnel test data before the wind tunnel test data is produced by a fan blade manufacturer, a model based on threshold value alarming cannot judge the complex condition, and the condition of false alarming often exists. In addition, because the wind field adopts different fan models and different blade manufacturers, algorithm updating optimization cannot be carried out according to the actual running condition of the wind field by adopting different threshold value alarming, and therefore the accuracy of an alarming model is reduced. Therefore, a fan blade vibration alarm method is urgently needed, the accuracy of fault classification can be improved, and the capture of the generator fault of the wind turbine generator and the identification of different fault types are realized.
Disclosure of Invention
The present invention is directed to solving, at least in part, one of the technical problems in the related art.
Therefore, the first objective of the present invention is to provide a method for alarming fan blade vibration, so as to capture the generator fault of a wind turbine and identify different fault types, thereby reducing the number of false alarms.
The second purpose of the invention is to provide a fan blade vibration alarm device.
A third object of the invention is to propose a computer device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a method for alarming a fan blade vibration, including:
acquiring a frequency domain curve of fan blade vibration, an alarm actual measurement value and an alarm threshold value of a natural frequency amplitude index;
training a pre-constructed hybrid model according to the frequency domain curve of the fan blade vibration, the natural frequency amplitude index alarm measured value and the alarm threshold value to obtain a trained hybrid model;
and acquiring an actual measurement frequency domain signal of the wind turbine generator, and inputting the actual measurement frequency domain signal of the wind turbine generator into the trained hybrid model to obtain a vibration alarm result of the blade of the wind turbine generator.
Optionally, in an embodiment of the present invention, the hybrid model includes an input layer, a convolutional neural network layer, a long and short term memory network layer, an attention layer, and an output layer, and the training of the pre-constructed hybrid model according to the frequency domain curve of the fan blade vibration, the measured natural frequency amplitude index alarm value, and the alarm threshold value is performed to obtain the trained hybrid model, further including:
inputting the frequency domain curve of the fan blade vibration, the measured alarm value of the natural frequency amplitude index and the alarm threshold value into the input layer, and obtaining input vectors corresponding to the training samples through the input layer;
inputting the input vector into the convolutional neural network layer, and performing feature extraction on the input vector to screen out a target feature vector;
acquiring the target characteristic vectors, and inputting the target characteristic vectors into the long-short term memory network layer to extract the characteristic relations corresponding to the target characteristic vectors;
inputting the feature relations corresponding to the target feature vectors into the attention layer, and screening the feature relations according to the attention weight parameter values of the feature relations in the attention layer to obtain output vectors;
and inputting the output vector to the output layer, and outputting a vibration alarm result.
Optionally, in an embodiment of the present invention, the convolutional neural network layer includes a convolutional layer and a discard layer, and the inputting the input vector into the convolutional neural network layer and performing feature extraction on the input vector to screen out a target feature vector includes:
inputting the input vector into the convolutional layer to obtain a plurality of characteristic vectors of the input vector extracted by the convolutional layer;
inputting a plurality of feature vectors of the input vector to the discarding layer to screen out a target feature vector from the plurality of features.
Optionally, in an embodiment of the present invention, after inputting the output vector to the output layer and outputting a vibration alarm result, the method further includes:
and acquiring the vibration alarm result, and performing inverse normalization processing on the vibration alarm result.
Optionally, in an embodiment of the present invention, after the obtaining the vibration alarm result and performing inverse normalization processing on the vibration alarm result, the method further includes:
the vibration alarm result is subjected to inverse normalization to obtain an actual vibration alarm result:
wherein the content of the first and second substances,
for bearing alarm data before the anti-normalization process predicted by the hybrid network,
for the anti-normalized blade vibration alarm data, y min 、y max Which are respectively the minimum value and the maximum value in the historical output data before normalization processing.
In order to achieve the above object, a second embodiment of the present invention provides a fan blade vibration warning device, including:
the first acquisition module is used for acquiring a frequency domain curve of fan blade vibration, an alarm actual measurement value of a natural frequency amplitude index and an alarm threshold value;
the training module is used for training a pre-constructed hybrid model according to the frequency domain curve of the fan blade vibration, the natural frequency amplitude index alarm measured value and the alarm threshold value to obtain a trained hybrid model;
and the second acquisition module is used for acquiring the actually measured frequency domain signal of the wind turbine generator, and inputting the actually measured frequency domain signal of the wind turbine generator into the trained hybrid model to obtain the vibration alarm result of the blades of the wind turbine generator.
Optionally, in an embodiment of the present invention, the training module is further configured to:
inputting the frequency domain curve of the fan blade vibration, the measured alarm value of the natural frequency amplitude index and the alarm threshold value into the input layer, and obtaining input vectors corresponding to the training samples through the input layer;
inputting the input vector into the convolutional neural network layer, and performing feature extraction on the input vector to screen out a target feature vector;
acquiring the target characteristic vectors, and inputting the target characteristic vectors into the long-short term memory network layer to extract the characteristic relations corresponding to the target characteristic vectors;
inputting the feature relations corresponding to the target feature vectors into the attention layer, and screening the feature relations according to the attention weight parameter values of the feature relations in the attention layer to obtain output vectors;
and inputting the output vector to the output layer, and outputting a vibration alarm result.
Optionally, in an embodiment of the present invention, the apparatus is further configured to:
inputting the input vector into the convolutional layer to obtain a plurality of characteristic vectors of the input vector extracted by the convolutional layer;
inputting a plurality of feature vectors of the input vector to the discarding layer to screen a target feature vector from the plurality of features.
In summary, in the method and the device for alarming fan blade vibration provided by the present invention, a frequency domain curve of fan blade vibration, an alarm measured value of a natural frequency amplitude index and an alarm threshold are obtained first; training a pre-constructed hybrid model according to a frequency domain curve of fan blade vibration, an alarm actual measurement value of a natural frequency amplitude index and an alarm threshold value to obtain a trained hybrid model; and acquiring an actual measurement frequency domain signal of the wind turbine generator, and inputting the actual measurement frequency domain signal of the wind turbine generator into the trained hybrid model to obtain a vibration alarm result of the blades of the wind turbine generator. Based on the method, the logic relation between the input parameters and the target parameters established according to the hybrid model is more accurate and reliable, the capture of the generator faults of the wind turbine generator and the recognition of different fault types can be realized, and therefore the number of times of false alarms can be reduced.
In order to achieve the above object, a third aspect of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the computer device implements the method according to the first aspect of the present invention.
To achieve the above object, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method of the first aspect of the present invention.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a fan blade vibration alarm method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a neuron in a drop layer network according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a hybrid model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a fan blade vibration alarm device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention and should not be construed as limiting the present invention.
A fan blade vibration warning method and apparatus according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a fan blade vibration alarm method according to an embodiment of the present invention.
Step S1: and acquiring a frequency domain curve of the fan blade vibration, an alarm actual measurement value and an alarm threshold value of the natural frequency amplitude index.
In the embodiment of the present invention, the alarm threshold may be used to trigger a vibration alarm when the measured alarm value exceeds the alarm threshold.
Step S2: and training the pre-constructed hybrid model according to the frequency domain curve of the fan blade vibration, the alarm actual measurement value of the natural frequency amplitude index and the alarm threshold value to obtain the trained hybrid model.
In an embodiment of the present invention, the hybrid model includes an input layer, a convolutional neural network layer, a long-short term memory network layer, an attention layer, and an output layer.
And in the embodiment of the invention, the pre-constructed hybrid model is trained according to the frequency domain curve of the fan blade vibration, the natural frequency amplitude index alarm measured value and the alarm threshold value to obtain the trained hybrid model, and the method further comprises the following steps:
inputting a frequency domain curve of fan blade vibration, an alarm actual measurement value of a natural frequency amplitude index and an alarm threshold value into an input layer, and obtaining input vectors corresponding to training samples through the input layer;
inputting the input vector into a convolutional neural network layer, and performing feature extraction on the input vector to screen out a target feature vector;
acquiring target characteristic vectors, and inputting the target characteristic vectors into a long-term and short-term memory network layer to extract characteristic relations corresponding to the target characteristic vectors;
inputting the feature relations corresponding to the target feature vectors into an attention layer, and screening the feature relations according to the attention weight parameter values of the feature relations in the attention layer to obtain output vectors;
and inputting the output vector to an output layer, and outputting a vibration alarm result.
And, in an embodiment of the present invention, the convolutional neural network layer includes a convolutional layer and a discard layer, the convolutional neural network layer inputs the input vector to the convolutional neural network layer, and performs feature extraction on the input vector to screen out the target feature vector, including:
inputting the input vector into the convolutional layer, and acquiring a plurality of characteristic vectors of the input vector extracted by the convolutional layer;
a plurality of feature vectors of the input vector are input to a discarding layer to screen out a target feature vector from the plurality of features.
For example, in the embodiment of the present invention, after the frequency domain curve of the fan blade vibration, the measured alarm value of the natural frequency amplitude index and the alarm threshold value data set are preprocessed and similar data are selected, the data are input into the model through the input layer, the length of the batch input data may be m, and the available data may be m
Representing the input vector.
Inputting X into the convolutional neural network layer, and carrying out feature extraction on the input vector to screen out the target feature vector, at the moment, in order to screen out the target feature vector accurately, the convolutional neural network layer comprises a convolutional layer and a discarding layer, inputting the input vector into the convolutional neural network layer, and carrying out feature extraction on the input vector to screen out the target feature vector, the method comprises the following steps: the input vector is input into the convolutional layer, a plurality of feature vectors of the input vector extracted by the convolutional layer are obtained, and the plurality of feature vectors of the input vector are input into the discarding layer so as to screen out the target feature vector from the plurality of features. In the embodiment of the invention, under the condition that the data dimension of the solar power generation amount is 1 dimension, the convolution layer is selected to be one-dimensional convolution, the size of the convolution kernel is 3 at the moment, and the RELU activation function is used in a combined manner to obtain a plurality of characteristic vectors of the input vector. Furthermore, with the drop layer set to 0.2, half of the hidden neurons in the network of the drop layer can be temporarily randomly dropped and the input-output neurons remain unchanged.
It should be noted that fig. 2 is a schematic diagram of a neuron in a discard layer network provided in this embodiment. As shown in FIG. 2, the circles in FIG. 2 represent neurons that have not been deleted, and the circles with crosses represent neurons that have been deleted. And then, the input neurons are propagated forwards through the modified network, the obtained loss result is propagated backwards through the modified network, and after a small batch of training samples are executed, the parameters (w, b) corresponding to the neurons which are not deleted are updated according to a random gradient descent method. Where w is the parameter weight in the neural network and b is the bias in the neural network.
It is understood that after obtaining the updated corresponding parameters (w, b), in order to avoid the problem of overfitting the trained prediction model, in some embodiments, the process may be repeated continuously: and recovering deleted neurons, wherein the deleted neurons are kept as they are at the moment, the non-deleted neurons are updated, a half-size subset is randomly selected from the hidden neurons to be temporarily deleted, parameters of the deleted neurons are backed up, and for a small batch of training samples, the parameters (w, b) are updated according to a random gradient descent method after being propagated to the previous direction and then propagated to the reverse direction to be lost so as to solve the problem of overfitting of different networks.
In addition, in some embodiments, in the case that the missing layer is set to 0.2, if the number of neurons is n, there may be 0.2n neurons to be deleted, wherein one way to delete the neurons is to change the activation function value of the neurons in the network to 0 with probability p. If the output vector length is i, the target feature vector is H c ＝[h c1 …h c1 …] T Wherein the neurons of the discarded layer compute activation function values in the network
One way of calculating is:
wherein, the Bernoulli function generates a probability vector r, that is, a vector of 0 and 1 is randomly generated.
Then, inputting the obtained target characteristic vector into a Long-Short Term Memory artificial neural network layer, learning the vibration alarm behavior characteristic of the fan blade through the Long-Short Term Memory artificial neural network layer and a bidirectional Long-Short Term Memory (biLSTM) layer structure, and if the length of the first output vector is j, the first output vector of the Long-Short Term Memory artificial neural network layer is H L ＝[h L1 …h Lε …h L ] J Calculating H L One way of calculating is:
H l ＝max(dropout(L))+b r
wherein, the long-short term memory artificial neural network layer needs to be accessed to a discarding layer and a maximum pooling layer, max is a maximum function in the maximum pooling layer, b r For the bias of the pooling layer, L is the output of the long-short term memory artificial neural network layer, W C ,b c Respectively the weight and the bias of the long-short term memory artificial neural network layer.
Then, after the first output vectors corresponding to the target feature vectors are input to the attention layer, according to a weight distribution principle in the attention layer, the attention weight parameter values of the first output vectors are distributed to obtain the attention weight parameter values of the first output vectors, and the first output vectors are screened according to the attention weight parameter values of the first output vectors to obtain second output vectors. When the length of the second output vector is k, the second output vector S' is
And finally, after the second output vector is input into the output layer, the output layer obtains a predicted value of a vibration alarm result of the wind turbine generator blade through the full-connection layer, and if the predicted compensation of the output layer is n, the predicted value Y of the power generation amount of the photovoltaic is
An exemplary way to calculate Y is:
Y＝f(W r ·s+b r )
wherein, W r As output layer weights, b r For output layer biasing, f is the fully-connected layer activation function.
And, in the embodiment of the present invention, after inputting the output vector to the output layer and outputting the vibration alarm result, the method further includes:
and acquiring a vibration alarm result, and performing inverse normalization processing on the vibration alarm result.
Further, in the embodiment of the present invention, after obtaining the vibration alarm result and performing inverse normalization processing on the vibration alarm result, the method further includes:
the vibration alarm result is subjected to inverse normalization to obtain an actual vibration alarm result:
wherein the content of the first and second substances,
for the bearing alarm data obtained by mixed network prediction before reverse normalization, y is reverse
Normalized blade vibration alarm data, y min 、y max Which are respectively the minimum value and the maximum value in the historical output data before normalization processing.
Based on the foregoing embodiments, fig. 3 is a schematic structural diagram of a hybrid model according to an embodiment of the present invention. As shown in fig. 3, the hybrid model includes an input layer, a convolutional neural network layer, a long-short term memory artificial neural network layer, an attention layer and an output layer, and one embodiment of calculating the vibration alarm result of the wind turbine blades through the hybrid model is as follows: the method comprises the steps of inputting collected training samples into an input layer, converting the training samples into input vectors, extracting features when the training samples are input into a convolutional neural network layer, generating target feature vectors, inputting the target feature vectors into a long-short term memory artificial neural network layer, predicting a predicted value of a vibration alarm result of the wind turbine generator blades by the long-short term memory artificial neural network layer and an attention layer through learning rules in the target feature vectors extracted by the convolutional neural network layer, and outputting the predicted value through an output layer.
And step S3: and acquiring an actual measurement frequency domain signal of the wind turbine generator, and inputting the actual measurement frequency domain signal of the wind turbine generator into the trained hybrid model to obtain a vibration alarm result of the blades of the wind turbine generator.
In summary, in the fan blade vibration alarm method provided by the present invention, a frequency domain curve of fan blade vibration, an alarm measured value of a natural frequency amplitude index and an alarm threshold are obtained first; training a pre-constructed hybrid model according to a frequency domain curve of fan blade vibration, a natural frequency amplitude index alarm measured value and an alarm threshold value to obtain a trained hybrid model; and acquiring an actual measurement frequency domain signal of the wind turbine generator, and inputting the actual measurement frequency domain signal of the wind turbine generator into the trained hybrid model to obtain a vibration alarm result of the blades of the wind turbine generator. Based on the method, the logic relation between the input parameters and the target parameters established according to the hybrid model is more accurate and reliable, the capture of the generator faults of the wind turbine generator and the recognition of different fault types can be realized, and the times of false alarm can be reduced.
Fig. 4 is a schematic structural diagram of a fan blade vibration alarm device according to an embodiment of the present invention.
As shown in fig. 4, the fan blade vibration warning device includes:
the first obtaining module 100 is configured to obtain a frequency domain curve of the fan blade vibration, an actual alarm measurement value of a natural frequency amplitude index, and an alarm threshold;
the training module 200 is used for training a pre-constructed hybrid model according to a frequency domain curve of fan blade vibration, an alarm actual measurement value of an inherent frequency amplitude index and an alarm threshold value to obtain a trained hybrid model;
and a second obtaining module 300, configured to obtain an actual measurement frequency domain signal of the wind turbine generator, and input the actual measurement frequency domain signal of the wind turbine generator to the trained hybrid model to obtain a vibration alarm result of the wind turbine generator blade.
In an embodiment of the present invention, the training module 200 is further configured to:
inputting a frequency domain curve of fan blade vibration, an alarm actual measurement value of a natural frequency amplitude index and an alarm threshold value into an input layer, and obtaining input vectors corresponding to training samples through the input layer;
inputting the input vector into a convolutional neural network layer, and performing feature extraction on the input vector to screen out a target feature vector;
acquiring target characteristic vectors, and inputting the target characteristic vectors into a long-term and short-term memory network layer to extract characteristic relations corresponding to the target characteristic vectors;
inputting the feature relations corresponding to the target feature vectors into an attention layer, and screening the feature relations according to the attention weight parameter values of the feature relations in the attention layer to obtain output vectors;
and inputting the output vector to an output layer, and outputting a vibration alarm result.
And, in the embodiment of the present invention, the fan blade vibration alarm device is further configured to:
inputting the input vector into the convolutional layer, and acquiring a plurality of characteristic vectors of the input vector extracted by the convolutional layer;
a plurality of feature vectors of the input vector are input to a discarding layer to screen out a target feature vector from the plurality of features.
It should be noted that the foregoing explanation of the embodiment of the fan blade vibration alarm method is also applicable to the device of the embodiment, and reference may be made to the related description of the above embodiment, which is not repeated herein.
In summary, in the fan blade vibration alarm device provided by the present invention, a frequency domain curve of fan blade vibration, an alarm measured value of a natural frequency amplitude index and an alarm threshold are obtained first; training a pre-constructed hybrid model according to a frequency domain curve of fan blade vibration, an alarm actual measurement value of a natural frequency amplitude index and an alarm threshold value to obtain a trained hybrid model; and acquiring an actual measurement frequency domain signal of the wind turbine generator, and inputting the actual measurement frequency domain signal of the wind turbine generator into the trained hybrid model to obtain a vibration alarm result of the blades of the wind turbine generator. Based on the method, the logic relation between the input parameters and the target parameters established according to the hybrid model is more accurate and reliable, the capture of the generator faults of the wind turbine generator and the recognition of different fault types can be realized, and the times of false alarm can be reduced.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A fan blade vibration alarm method is characterized by comprising the following steps:
acquiring a frequency domain curve of fan blade vibration, an alarm actual measurement value and an alarm threshold value of a natural frequency amplitude index;
training a pre-constructed hybrid model according to the frequency domain curve of the fan blade vibration, the natural frequency amplitude index alarm measured value and the alarm threshold value to obtain a trained hybrid model;
and acquiring an actual measurement frequency domain signal of the wind turbine generator, and inputting the actual measurement frequency domain signal of the wind turbine generator into the trained hybrid model to obtain a vibration alarm result of the blade of the wind turbine generator.
2. The alarm method according to claim 1, wherein the hybrid model comprises an input layer, a convolutional neural network layer, a long-short term memory network layer, an attention layer and an output layer, and the training of the pre-constructed hybrid model according to the frequency domain curve of the fan blade vibration and the natural frequency amplitude index alarm measured value and the alarm threshold value results in a trained hybrid model, further comprising:
inputting the frequency domain curve of the fan blade vibration, the measured alarm value of the natural frequency amplitude index and the alarm threshold value into the input layer, and obtaining input vectors corresponding to the training samples through the input layer;
inputting the input vector into the convolutional neural network layer, and performing feature extraction on the input vector to screen out a target feature vector;
acquiring the target characteristic vectors, and inputting the target characteristic vectors into the long-short term memory network layer to extract the characteristic relations corresponding to the target characteristic vectors;
inputting the feature relations corresponding to the target feature vectors into the attention layer, and screening the feature relations according to the attention weight parameter values of the feature relations in the attention layer to obtain output vectors;
and inputting the output vector to the output layer, and outputting a vibration alarm result.
3. The alarm method of claim 2, wherein the convolutional neural network layer comprises a convolutional layer and a discard layer, and the inputting the input vector into the convolutional neural network layer and performing feature extraction on the input vector to screen out a target feature vector comprises:
inputting the input vector into the convolutional layer to obtain a plurality of characteristic vectors of the input vector extracted by the convolutional layer;
inputting a plurality of feature vectors of the input vector to the discarding layer to screen out a target feature vector from the plurality of features.
4. The alarm method of claim 2, after inputting the output vector to the output layer and outputting a vibration alarm result, further comprising:
and acquiring the vibration alarm result, and performing inverse normalization processing on the vibration alarm result.
5. The alarm method according to claim 4, wherein after said obtaining the vibration alarm result and performing an inverse normalization process on the vibration alarm result, further comprising:
the vibration alarm result is subjected to inverse normalization to obtain an actual vibration alarm result:
wherein, the first and the second end of the pipe are connected with each other,
for bearing alarm data obtained by the hybrid network prediction before the reverse normalization processing, y is reverse
Normalized blade vibration alarm data, y min 、y max The minimum value and the maximum value in the historical output data before normalization processing are respectively.
6. A fan blade vibration warning device, characterized by, includes:
the first acquisition module is used for acquiring a frequency domain curve of fan blade vibration, an alarm actual measurement value of a natural frequency amplitude index and an alarm threshold value;
the training module is used for training a pre-constructed hybrid model according to the frequency domain curve of the fan blade vibration, the natural frequency amplitude index alarm measured value and the alarm threshold value to obtain a trained hybrid model;
and the second acquisition module is used for acquiring the actually measured frequency domain signal of the wind turbine generator, and inputting the actually measured frequency domain signal of the wind turbine generator into the trained hybrid model to obtain the vibration alarm result of the blades of the wind turbine generator.
7. The warning device of claim 6, wherein the training module is further configured to:
inputting the frequency domain curve of the fan blade vibration, the measured alarm value of the natural frequency amplitude index and the alarm threshold value into the input layer, and obtaining input vectors corresponding to the training samples through the input layer;
inputting the input vector into the convolutional neural network layer, and performing feature extraction on the input vector to screen out a target feature vector;
acquiring the target characteristic vectors, and inputting the target characteristic vectors into the long-short term memory network layer to extract the characteristic relations corresponding to the target characteristic vectors;
inputting the feature relations corresponding to the target feature vectors into the attention layer, and screening the feature relations according to the attention weight parameter values of the feature relations in the attention layer to obtain output vectors;
and inputting the output vector to the output layer, and outputting a vibration alarm result.
8. The alarm device of claim 7, wherein the device is further configured to:
inputting the input vector into the convolutional layer to obtain a plurality of characteristic vectors of the input vector extracted by the convolutional layer;
inputting a plurality of feature vectors of the input vector to the discarding layer to screen out a target feature vector from the plurality of features.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any of claims 1-5 when executing the computer program.
10. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any one of claims 1-5.
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