CN111413075A - Fan base bolt loosening diagnosis method of multi-scale one-dimensional convolution neural network - Google Patents
Fan base bolt loosening diagnosis method of multi-scale one-dimensional convolution neural network Download PDFInfo
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Abstract
The invention relates to a method for diagnosing loosening of bolts of a fan base of a multi-scale one-dimensional convolutional neural network, and belongs to the technical field of mechanical state diagnosis. Firstly, a vibration time domain signal when a fan runs is used as the input of a multi-scale one-dimensional convolution neural network, dependence on signal processing and professional knowledge is eliminated, and the characteristics of an original signal are retained to the maximum extent; then, learning time domain signal features through the alternate multi-scale convolution layer and pooling layer; and finally, adding a Softmax multi-classifier behind the characteristic output layer, establishing mapping from the characteristic space to the health state space by utilizing backward propagation layer-by-layer fine tuning structural parameters, and outputting a diagnosis result of the looseness degree of the fan base bolt. The method integrates automatic learning of the loosening degree characteristic and the loosening degree diagnosis, and realizes intelligent diagnosis of the loosening degree of the bolts of the fan base. The feasibility and the effectiveness of the method are proved through diagnosis experiments on the loosening degree of the bolts of the fan base at a stable rotating speed and a variable rotating speed.
Description
Technical Field
The invention belongs to the technical field of mechanical state diagnosis, and relates to a method for diagnosing loosening of bolts of a fan base of a multi-scale one-dimensional convolutional neural network.
Background
The method is characterized in that a LL TSA algorithm is utilized by the existing fan base bolt loosening degree diagnosis method such as Hangulin to manually extract 24-dimensional vibration mixed domain characteristic parameters for feature reduction, a nearest neighbor classifier (KNNC) is combined to realize fan base bolt loosening degree diagnosis, a sense characteristic such as Chengxiang and the like is combined with a popular learning reduction structure, a low-dimensional characteristic set of the fan base bolt loosening degree is extracted by a weighted nearest neighbor classifier (WKNN), so that the fan base bolt loosening degree diagnosis method is difficult to effectively extract a large number of characteristic features of a fan base bolt loosening degree through a near-neighbor signal extraction technology, and the method is difficult to effectively extract a large number of characteristic features of the fan base bolt loosening degree by means of a near-neighbor signal extraction algorithm and a near-neighbor signal extraction technology.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for diagnosing loosening of a fan base bolt of a multi-scale one-dimensional convolutional neural network, wherein the used material has very similar physical and mechanical properties to the prototype material, so that the mechanical properties of surrounding rocks and a broken zone can be better simulated, and the raw materials have no toxic or side effect, do not cause damage to human bodies, are easily available, and have low price.
In order to achieve the purpose, the invention provides the following technical scheme:
the method for diagnosing the loosening of the bolts of the fan base of the multi-scale one-dimensional convolution neural network comprises the following steps of:
step 1: acquiring vibration time domain signals of different loosening degrees of a fan base bolt, and dividing a training sample and a test sample;
step 2: constructing a multi-scale one-dimensional convolution neural network model, and initializing model parameters;
and step 3: inputting training samples in a batch mode, propagating forward layer by layer to obtain refined sample characteristics, obtaining actual looseness state categories on an output layer, and calculating errors between expected output and actual output;
and 4, step 4: the error is propagated reversely, and the network parameters are finely adjusted layer by layer;
and 5: repeating the steps 3 and 4 until the training samples are completely trained and meet the network precision requirement or reach the iteration times, and obtaining a model for intelligently diagnosing the loosening degree of the bolts of the fan base;
step 6: inputting a test sample and obtaining a diagnosis result.
Optionally, the multi-scale one-dimensional convolutional neural network model in step 2 is composed of two alternating multi-scale convolutional layers, a pooling layer and two full-connected layers;
1) the multi-scale convolutional layer comprises n parallel convolutional layers, and each convolutional layer uses a one-dimensional convolutional core with different scales to perform convolution on input signals with different scales so as to extract signal characteristics with different finenesses; then, splicing the extracted features of each convolution layer and outputting the features to the next layer; the multiscale one-dimensional convolution is defined as follows:
wherein the content of the first and second substances,
representing the ith output characteristic in the convolution kernel scale K, X representing the input signal, Wi kRepresenting the i-th convolution kernel at kernel scale k, biRepresenting the bias applied when the ith feature is output, the convolution kernel scale K ═ K1，k2…kn]F denotes an activation function; the activation function used here is the Relu activation function, as shown below;
f(x)＝max(0，x)
2) the pooling layer is used for carrying out scaling mapping on the data of the previous layer, and input data is subjected to sub-sampling through pooling core, so that the spatial dimension of the input data is reduced;
yi＝f(βidown(x)+bi)
where down is a down-sampling function, βiWeight representing the ith feature, x corresponding to the output of the previous layer, i.e. convolutional layer, biA bias representing the ith characteristic;
3) each neuron in the full-connection layer is fully connected with all neurons in the previous layer, and local information with category distinctiveness in the convolution layer or the pooling layer is integrated; a hidden layer is connected behind the full connection layer, and finally, classification is finished by a Softmax multi-classifier; assuming a classification problem by class k, the output of the Softmax multi-classifier is calculated as follows:
in the formula: w and b are the weight matrix and bias value, respectively, and O is the final output of the convolutional neural network
The invention has the beneficial effects that:
1) the original vibration time domain signal is used as input, dependence on signal processing and professional knowledge is eliminated, the workload of signal preprocessing is reduced, a one-dimensional network structure is operated to process the one-dimensional time domain signal, and deep features of the signal are automatically extracted.
2) A multi-scale convolutional layer is designed in the network, a plurality of convolution kernels with different scales are arranged in the multi-scale convolutional layer, and the convolution kernels have the characteristics of local receptive fields and weight sharing, so that the parameter calculation amount can be effectively reduced; the convolution kernels with different scales can extract features with different fineness, and the feature extraction capability of the network can be effectively enhanced by arranging a plurality of convolution kernels with different scales, so that the extraction of the state features of the loosening degree of the bolts of the fan base at different operating speeds is adapted.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a multi-scale one-dimensional convolution neural network structure
FIG. 3 is a multi-scale convolutional layer structure
FIG. 4 is a graph of diagnostic results of various methods; FIG. 4(a) is a diagnostic result of bolt looseness of a fan base by different CNN methods at a stable rotation speed of a fan; FIG. 4(b) is a diagnostic result of the loosening degree of bolts of a fan base by different CNN methods under variable rotation speed of the fan;
FIG. 5 is a visualization of different method features. 5(a) and 5(e) are respectively visualization graphs of the characteristics of the invention at a stable rotating speed and a variable rotating speed; fig. 5(b) and 5(f) are single-scale one-dimensional CNN method feature visualization graphs at a stable rotation speed and a variable rotation speed, respectively; fig. 5(c) and 5(g) are two-dimensional CNN method feature visualization graphs at a stable rotation speed and a variable rotation speed, respectively; fig. 5(d) and 5(h) are feature visualization graphs of the manual feature extraction method under the stable rotation speed and the variable rotation speed, respectively.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Hereinafter, preferred embodiments of the present invention will be described in detail as shown in fig. 1 to 3.
1) Acquiring vibration time domain signals of different loosening degrees of bolts of a fan base, wherein part of the vibration time domain signals are organized into training samples, and part of the vibration time domain signals are organized into testing samples;
2) constructing a multi-scale one-dimensional convolution neural network model, and initializing model parameters;
3) inputting training samples in a batch mode, propagating forward layer by layer to obtain refined sample characteristics, obtaining actual looseness state categories on an output layer, and calculating errors between expected output and actual output;
4) and (5) reversely propagating the error, and finely adjusting the network parameters layer by layer.
5) Repeating the steps 3 and 4 until the training samples are completely trained and the network precision requirement is met or the iteration times are reached, and obtaining a model for intelligently diagnosing the loosening degree of the bolts of the fan base;
6) inputting a test sample and obtaining a diagnosis result.
The present example is illustrated below:
the first step is as follows: a vibration signal to be analyzed is received. The original signals in the embodiment are vibration time domain signals of different loosening degrees (full tightness and loosening of 1-4 bolts) of base bolts under the conditions that the fan is in stable rotation speed (1500r/min) and variable rotation speed (the speed is increased from 500r/min to 1500r/min and the speed is decreased from 1500r/min to 500 r/min). The sampling frequency is 25.6kHz, and the sampling length is 100 k. The training and testing samples are intercepted in the form of a sliding window, the length of the sliding window is 1024, and the moving step length is 512. Experiments are respectively carried out under the stable rotating speed and the variable rotating speed of the fan. Under the stable rotational speed, for 5 kinds of fan foundation connecting bolt looseness degree states, the number of samples of each state is 190, and 950 samples are counted. And the ratio of 7: 3, the training set is divided into 665 samples and the test set is 285 samples. At variable rotation speed, the sampling number of each loosening degree state is 380 (including 190 samples of two variable rotation speed states of fan speed increasing and speed reducing), and 1900 samples are counted, wherein the sampling number is 7: 3, the training set is 1330 samples and the test set is 570 samples.
The second step is that a multi-scale one-dimensional convolutional neural network model is constructed, aiming at the design thought of two layers of multi-scale convolutional layers, a convolutional kernel with a larger scale range is set in the first layer of the multi-scale convolutional layer to extract different fineness characteristics with a wider range, a convolutional kernel with a relatively smaller scale range is used in the second layer of the multi-scale convolutional layer to refine the extracted different fineness characteristics again and extract abstract characteristics of a higher signal layer, the scales of the convolutional kernels of the first layer of the multi-scale convolutional layer are 1 ×, 1 × and 1 × respectively, the scales of the convolutional kernels of the second layer are 1 ×, 1 8610 and 1 × respectively, the setting step lengths of the two layers of the multi-scale convolutional layer are 3 and 2 respectively, a padding mode is set as same padding to avoid edge information loss, the output dimensions of the parallel convolutional layers in the multi-scale convolutional layers are ensured to be consistent, so that characteristic splicing is convenient, the regularization model sets a pooling mode as maximum value, the pooling of the two layers of the pooling layers are 1, 1 ×, 2, the number of all-link units is set as a full link coefficient of an adaptive learning probability distribution, and an optimal coefficient of an adaptive learning probability distribution of an adaptive learning probability of a first layer, a full link prediction coefficient of an adaptive prediction coefficient of a full link optimization coefficient of a full link optimization coefficient of a full link.
The third step: inputting training samples in a batch mode (the batch size is 128), propagating forwards layer by layer to obtain refined sample characteristics, obtaining actual looseness state categories in an output layer, and calculating errors between expected output and actual output;
the fourth step: the error is propagated reversely, and the network parameters are finely adjusted layer by layer;
the fifth step: repeating the steps 2 and 3 until the training samples are completely trained and the network precision requirement is met or the iteration times are reached, and obtaining a model for intelligently diagnosing the loosening degree of the bolts of the fan base;
and a sixth step: inputting a test sample and obtaining a diagnosis result.
The method comprises the steps of observing a graph 4, (a) a diagnosis result of the loosening degree of bolts of a fan base of different CNN methods under the condition that the fan is in stable rotating speed, (b) a diagnosis result of the loosening degree of bolts of the fan base of different CNN methods under the condition that the fan is in variable rotating speed, wherein the diagnosis precision of each group is continuously improved along with the increase of the iteration times, the diagnosis precision of the method provided by the invention reaches more than 99% under the condition that the iteration times are 50 times, the diagnosis precision fluctuates stably along with the increase of the iteration times, the accuracy of a single-scale one-dimensional CNN iteration is 80.15%, the accuracy of a two-dimensional CNN reaches only 32.12%, the fluctuation is obvious, the method provided by the invention also reaches 97.19% high accuracy under the condition that the iteration times are 50, the other two methods are also better, firstly, a one-dimensional network model is more suitable for processing of one-dimensional time domain signals, secondly, the characteristic extraction is carried out by using a plurality of convolution kernels with different-scale convolution layers for characteristic extraction, the higher diagnosis precision can be obtained, the advantages of the characteristic extraction capability are indirectly shown, the method can be obtained after 10 times of training tests, the average accuracy is obtained, the calculation of the average accuracy of the obtained by using a theoretical calculation of a conventional theoretical calculation of a theoretical model, the characteristic extraction of a theoretical verification method, the characteristic extraction of a theoretical calculation of a theoretical model, the theoretical model is more accurate characteristic extraction of a theoretical model is more than the theoretical model, the theoretical.
TABLE 1 diagnosis results of different methods at steady rotational speed
TABLE 2 diagnostic results for different methods at variable rotational speed
Looking at fig. 5, fig. 5 shows the analysis and visualization of the features proposed by each method by using t-Distributed Stochastic neighbor embedding (t-SNE). (a) Respectively showing the characteristic visualization graphs of the invention under the stable rotating speed and the variable rotating speed; (b) and (f) are single-scale one-dimensional CNN method characteristic visualization graphs under stable rotating speed and variable rotating speed respectively; (c) and (g) two-dimensional CNN method characteristic visualization graphs under stable rotating speed and variable rotating speed respectively; (d) and (h) feature visualization graphs of the manual feature extraction method under the stable rotating speed and the variable rotating speed respectively; by comparing the characteristic visual images, for the manual characteristic extraction algorithm, a plurality of parts of extracted characteristics are mixed together, the characteristic separation degree is not high, and the loosening degree state of the bolts of the fan base cannot be effectively represented; compared with the visualization results of the CNN algorithm characteristics of all groups, the same looseness state characteristics extracted by the method are well gathered together, the characteristics of different looseness states are effectively separated, and the characteristics extracted by the other two CNN algorithms are partially mixed.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (2)
1. The method for diagnosing the loosening of the bolts of the fan base of the multi-scale one-dimensional convolution neural network is characterized by comprising the following steps of: the method comprises the following steps:
step 1: acquiring vibration time domain signals of different loosening degrees of a fan base bolt, and dividing a training sample and a test sample;
step 2: constructing a multi-scale one-dimensional convolution neural network model, and initializing model parameters;
and step 3: inputting training samples in a batch mode, propagating forward layer by layer to obtain refined sample characteristics, obtaining actual looseness state categories on an output layer, and calculating errors between expected output and actual output;
and 4, step 4: the error is propagated reversely, and the network parameters are finely adjusted layer by layer;
and 5: repeating the steps 3 and 4 until the training samples are completely trained and meet the network precision requirement or reach the iteration times, and obtaining a model for intelligently diagnosing the loosening degree of the bolts of the fan base;
step 6: inputting a test sample and obtaining a diagnosis result.
2. The intelligent diagnosis method for the loosening degree of the bolts of the fan base as claimed in claim 1, wherein: the multi-scale one-dimensional convolution neural network model in the step 2 consists of two alternate multi-scale convolution layers, a pooling layer and two full-connection layers;
1) the multi-scale convolutional layer comprises n parallel convolutional layers, and each convolutional layer uses a one-dimensional convolutional core with different scales to perform convolution on input signals with different scales so as to extract signal characteristics with different finenesses; then, splicing the extracted features of each convolution layer and outputting the features to the next layer; the multiscale one-dimensional convolution is defined as follows:
wherein the content of the first and second substances,
representing the ith output characteristic in the convolution kernel scale K, X representing the input signal, Wi kRepresenting the i-th convolution kernel at kernel scale k, biRepresenting the bias applied when the ith feature is output, the convolution kernel scale K ═ K1，k2…kn]F denotes an activation function; the activation function used here is the Relu activation function, as shown below;
f(x)＝max(0，x)
2) the pooling layer is used for carrying out scaling mapping on the data of the previous layer, and input data is subjected to sub-sampling through pooling core, so that the spatial dimension of the input data is reduced;
yi＝f(βidown(x)+bi)
where down is a down-sampling function, βiWeight representing the ith feature, x corresponding to the output of the previous layer, i.e. convolutional layer, biA bias representing the ith characteristic;
3) each neuron in the full-connection layer is fully connected with all neurons in the previous layer, and local information with category distinctiveness in the convolution layer or the pooling layer is integrated; a hidden layer is connected behind the full connection layer, and finally, classification is finished by a Softmax multi-classifier; assuming a classification problem by class k, the output of the Softmax multi-classifier is calculated as follows:
in the formula: w and b are the weight matrix and bias value, respectively, and o is the final output of the convolutional neural network.
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| CN114299421A (en) * | 2021-12-20 | 2022-04-08 | 北京理工大学 | Method for detecting bolt looseness, convolutional neural network, training method and system |
| CN114266280A (en) * | 2021-12-29 | 2022-04-01 | 西安交通大学 | Bolt loosening state identification method based on improved convolutional neural network |
| CN114266280B (en) * | 2021-12-29 | 2023-10-13 | 西安交通大学 | Bolt loosening state identification method based on improved convolutional neural network |
| CN116026586A (en) * | 2023-01-31 | 2023-04-28 | 东华大学 | Harmonic reducer delivery qualification judging method and device |
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