CN206480004U - Adaptive deep confidence network bearing fault diagnosis device based on Nesterov momentum method - Google Patents
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Abstract
The utility model relates to a self-adaptive depth confidence network bearing fault diagnosis device based on a Nesterov momentum method, which comprises a signal acquisition module, a signal acquisition module and a signal processing module, wherein the signal acquisition module is used for acquiring original signals of different health states of a rolling bearing; the fault diagnosis module is connected with the signal acquisition module, takes the original signal as an input signal and automatically extracts deep features of the original signal; and the unknown state signal input module is connected with the fault diagnosis module, and the fault diagnosis module is used for judging the working state and the fault type of the rolling bearing. The utility model discloses on antifriction bearing fault classification's basis, to present degree of depth Belief Network (Deep Belief Network) to the limited problem of the fault classification precision of original signal, add Nesterov momentum method and independent self-adaptation learning rate for DBN trains speed in advance, improves fault classification precision.
Description
Technical Field
The utility model relates to a rolling bearing fault diagnosis's technical field especially relates to an adaptive depth confidence network bearing fault diagnosis device based on Nesterov momentum method.
Background
Rolling bearings are the most widely used and easily damaged parts in the aerospace, electrical, petrochemical, metallurgical, and mechanical industries. The operating conditions of the rotating machine are closely related to the rolling bearings, and it is statistically estimated that about 30% of mechanical failures are associated with bearing damage in the rotating machine equipment using the rolling bearings. In case of accidental failure of mechanical equipment, the normal production process and the product quality are affected and therefore huge economic losses occur, and serious persons even endanger the personal safety, resulting in serious catastrophic accidents. Based on the improvement of the reliability of the bearing and the guarantee of the safe operation of the mechanical equipment, a detection mode is necessary to detect the health state of the bearing, identify whether a fault occurs, and further take necessary measures to prevent the bearing from being damaged and guarantee the safe operation of the mechanical equipment.
For diagnostics, a good feature expression plays a key role in the accuracy of pattern recognition. At present, a great number of rolling bearing fault diagnosis technologies rely on manual feature extraction, such as SIFH, SVM, LBP and the like, and a great deal of time is spent on feature extraction. In addition, different characteristics have different expression meanings, and uniform characteristics suitable for different models are difficult to find, so that the manual characteristic selection is time-consuming and requires heuristic professional knowledge. In 2006, professor toronto university of toronto canada, tabo Geoffrey Hinton in the field of machine Learning and his student Ruslan salakhatdinov published an article in the american journal "Science" that proposed Deep Learning (Deep Learning), the main ideas included: the neural network of the multiple hidden layers is similar to a human visual system, can extract more abstract feature expression of data, has excellent feature learning capability, and compared with the traditional method, the deep learning neural network automatically extracts deeper features of the data by constructing a multilayer network to represent the data, so that the accuracy of classification and prediction is improved. However, most of the existing DBN models take artificially extracted features as input, and on the basis of the artificially extracted features, deeper features are extracted for fault classification, such as wavelet packet energy features, which still requires quite professional knowledge, and meanwhile, the fault classification effect of the DBN diagnostic model directly performing deep feature extraction on an original signal still needs to be improved.
In view of the above defects, the designer actively makes research and innovation to create a Nesterov momentum method-based adaptive Deep Belief Network (Deep Belief Network) bearing fault diagnosis device, so that the device has industrial utilization value.
SUMMERY OF THE UTILITY MODEL
In order to solve the technical problem, the utility model aims at providing an adaptive degree of depth confidence network bearing fault diagnosis device based on Nesterov momentum method.
The utility model discloses a self-adaptation degree of depth confidence network bearing fault diagnosis device based on Nesterov momentum method, include
A signal acquisition module for acquiring raw signals of different health states of the rolling bearing;
the fault diagnosis module is connected with the signal acquisition module and takes the original signal as an input signal to automatically extract deep features of the original signal;
an unknown state signal input module connected with the fault diagnosis module, wherein the fault diagnosis module judges the working state and the fault type of the rolling bearing.
Further, the fault diagnosis module comprises a DBN model building module and a Softmax classifier which are connected, and the DBN model building module is also connected with the signal acquisition module and the unknown state signal input module at the same time.
Furthermore, the signal acquisition module and the unknown state signal input module are connected with the DBN model building module through a signal classification module.
Furthermore, the signal acquisition module is a DAS data acquisition system and is used for acquiring vibration signals of the rolling bearings.
Borrow by above-mentioned scheme, the utility model discloses at least, have following advantage: different from the traditional artificial feature extraction fault diagnosis technology, the utility model discloses utilize the deep learning method of degree of depth confidence network with the original data as input signal, directly carry out automatic extraction to the deep characteristic of original signal, need not artifical the selection, effectively excavate the essential features of data, reduce time and the cost of artifical extraction characteristic; the utility model discloses on range upon range of limited boltzmann mechanism build degree of depth confidence network's basis, through adding Nesterov momentum method and independent self-adaptation learning rate, to the limited problem of the fault classification precision of present DBN model to original signal, effectively improved model training speed and antifriction bearing fault diagnosis precision for fault classification precision reaches 98.6%.
The above description is only an overview of the technical solution of the present invention, and in order to make the technical means of the present invention clearer and can be implemented according to the content of the description, the following detailed description is made with reference to the preferred embodiments of the present invention and accompanying drawings.
Drawings
FIG. 1 is a frame diagram of the adaptive deep confidence network bearing fault diagnosis device based on the Nesterov momentum method;
FIG. 2 is a flow chart of the adaptive deep belief network bearing fault diagnosis method based on the Nesterov momentum method according to the present invention;
FIG. 3 is a diagram of a restricted Boltzmann machine;
FIG. 4 is a diagram of a deep belief network architecture;
FIG. 5 is a bearing fault classification based on a Softmax regression model;
FIG. 6 is a time domain diagram of vibration signals of different health states of a bearing according to an embodiment of the present invention;
FIG. 7 is a diagram of the classification result of bearing fault training in the embodiment of the present invention;
FIG. 8 is a diagram of the classification result of the bearing fault test in the embodiment of the present invention;
fig. 9 is a schematic view of a rolling bearing data generation test stand.
Detailed Description
The following detailed description of the embodiments of the present invention is provided with reference to the accompanying drawings and examples. The following examples are intended to illustrate the invention, but are not intended to limit the scope of the invention.
Referring to fig. 1, the adaptive deep belief network bearing fault diagnosis apparatus according to a preferred embodiment of the present invention includes a signal acquisition module for acquiring original signals of different health states of a rolling bearing; the fault diagnosis module is connected with the signal acquisition module, takes the original signal as an input signal and automatically extracts deep features of the original signal; and the unknown state signal input module is connected with the fault diagnosis module, and the fault diagnosis module judges the working state and the fault type of the rolling bearing. In order to improve the model training speed and the fault diagnosis precision of the rolling bearing, the fault diagnosis module comprises a DBN model construction module and a Softmax classifier which are connected, and the DBN model construction module is also connected with the signal acquisition module and the unknown state signal input module at the same time; in order to further improve the fault classification precision, the signal acquisition module and the unknown state signal input module are connected with the DBN model building module through the signal classification module. As the utility model discloses a preferred embodiment, the utility model discloses a signal acquisition module is DAS data acquisition system, gathers antifriction bearing's vibration signal.
The working principle of the utility model is as follows:
the utility model discloses the self-adaptation degree of depth confidence network bearing fault diagnosis method based on Nesterov momentum method that corresponds, as shown in FIG. 2, include following step:
step 1: carrying out sample division on original signals of different health states of the rolling bearing to generate training samples;
step 2: building a DBN model through a laminated RBM, inputting a training sample into the DBN model, combining a batch random gradient descent method and a greedy layer-by-layer unsupervised algorithm to pre-train the DBN model, aiming at the problem that the fault classification precision of the original signal of the existing DBN model is limited, adding a Nesterov momentum method and an independent self-adaptive learning rate into the DBN model, accelerating the training speed and improving the fault classification precision;
and step 3: accessing a Softmax classifier at the top layer of the pre-trained model, individually training the Softmax classifier by using a supervision algorithm, and then performing global fine adjustment by using a BP algorithm and a conjugate gradient method to obtain optimal parameters of the model;
and 4, step 4: inputting unknown state signals to form a test sample set, and inputting the test sample into the trained DBN model to judge the fault type of the rolling bearing;
further, the step 1 specifically includes the following steps:
step 1.1: according to the method, vibration signals of the rolling bearing in different health states are used as original data X, and the number of signal data acquired in one period of bearing rotation can be represented as follows:
wherein: n is the number of signal data acquired in one period of the rotation of the bearing, n is usually rounded upwards in practical application, r is the rotation speed of the bearing and is given by r/min, f is the sampling frequency and is given by Hz;
step 1.2: dividing vibration signals of different health states of the bearing into training sample sets X 'with n as one sample length, wherein X'(g)∈RnAnd setting a label L for each health state, wherein L(g)∈ {1,2, 3.., k }, wherein L(g)Represents the g-th training sampleK represents a classification category;
further, the step 2 specifically includes the following steps:
step 2.1: the DBN model is built by stacking RBMs, and the method specifically comprises the following steps:
each RBM model comprises a visible layer v ═ v1,v2,...,viH and a layer h ═ h1,h2,...,hjHidden layer as in fig. 3, all visible and hidden elements are binary variables, i.e.vi∈{0,1},hj∈ {0,1}, i and j respectively represent the ith visual unit of the visual layer and the jth hidden unit of the hidden layer, a DBN model is built by stacking a plurality of RBM models from bottom to top as shown in FIG. 4, and the hidden layer h of the bottom RBM1Visual layer v as the previous layer RBM2Then this layer is the hidden layer h of RBM2Visual layer v as the previous layer RBM3In this way, when there is input in the visible layer of the bottom RBM, the hidden layer of the top RBM will extract the deep features corresponding to the input, because there is connection between RBM layers, there is no connection in the layers, when the state of the visible unit is given, the activation states of the hidden units are independent, and at this time, the activation probability of the jth hidden unit is:
wherein,for sigmoid activation of function, θ ═ Wij,ai,bjIs a parameter of RBM, WijRepresenting the weight of the connection between visible unit i and hidden unit j, aiRepresenting the offset of the visual cell i, bjRepresenting the bias of the hidden unit j. Since the structure of the RBM is symmetrical, when a hidden unit is givenIn the state, the activation condition of each visible unit is also condition-independent, that is, the activation probability of the ith visible unit is:
step 2.2: inputting a training sample into a DBN model, and pre-training the DBN model by combining a batch stochastic gradient descent method and a greedy layer-by-layer algorithm, wherein the pre-training method specifically comprises the following steps:
normalizing the training sample set X 'to make the amplitude range of the sample set X' between (0,1) to obtain a new sample set X ', and randomly dividing the sample set X' into a plurality of batches B ═ B ═ according to a batch random gradient descent method1,b2,...,bmAnd (5) inputting each batch sample set into the DBN model, pre-training by using a greedy layer-by-layer unsupervised algorithm, and firstly training a bottom RBM (radial basis function) which is recorded as the RBM1Inputting the batch sample set B into the RBM1For finding the appropriate value of the parameter theta to fit given training data, by maximizing the log-likelihood function learning of the RBM on the training set B, i.e.
Wherein P (v | θ) is a marginal distribution of the joint probability distribution P (v, h | θ), and (v, h) the joint probability is:
wherein Z (theta) is a normalization factor, and the expression is as follows:
e (v, h | θ) is the energy function of RBM:
in order to obtain the optimum parameter theta*And (3) performing gradient descent by using a CD algorithm, wherein the updating criterion of each parameter is as follows:
wherein, ηw，ηaAnd ηbThe learning rates of the weight, the visible layer bias and the hidden layer bias respectively,<·>reconrepresenting the distribution of model definition after one-step Gibbs sampling, and sequentially inputting each batch sample set into RBM1Training parameter θ1Inputting training sample B into the well-trained RBM1Obtaining extracted features F1＝{f1,f2,...,fmTherein ofl1Representing the first order sample feature length, in this case F1＝{f1,f2,...,fmB is original data B ═ B1,b2,...,bmThe first-order characteristic representation of the layer, namely the expression mode of the bottom layer; then the feature F1As the upper layer RBM (denoted as RBM)2) Input of (2), fixed parameter theta1Repeating the above steps to train RBM2Parameter theta of2To obtain a second order feature F2＝{f1,f2,...,fmTherein of l2Representing second order sample featuresA length; f2＝{f1,f2,...,fmIs a more abstract representation of features; then, the same strategy is adopted for the RBMs of the subsequent layers, namely, the output of the previous layer is taken as the input of the next layer to be trained in sequence, and the parameters of other layers are fixed and kept unchanged during the training of the parameters of each layer, and finally, the hidden layer output F of the RBM of the top layer is obtaineds＝{f1,f2,...,fmS is ≧ 2), whereinlsRepresents the characteristic length of s-order sample, in this case Fs＝{f1,f2,...,fmDeep feature expression extracted by a DBN model;
step 2.3: aiming at the problem that the accuracy of the existing DBN model for classifying the faults of the original signals is limited, a Nesterov momentum method and an independent self-adaptive learning rate are added into the DBN model, the pre-training speed is increased, and the classification accuracy is improved, and the method specifically comprises the following steps:
when a ravine problem is encountered by using the batch random gradient descent method (the gradient in one direction is obviously steeper than that in the other directions, and most of the cases correspond to a local minimum), the batch random gradient descent method does not accelerate to fall to the local minimum along the ravine, but oscillates repeatedly in the vicinity, in order to accelerate convergence and reduce oscillation, a general DBN model uses a momentum method during parameter updating, namely, a gradient at the last updating is multiplied by a factor γ (generally set to 0.9), and then the gradient at the last updating is added, if the directions of the two gradients are similar, the movement in the direction is accelerated, the convergence is accelerated, and the oscillation is reduced:
however, the momentum method is blind, cannot judge where the next parameter θ is going to fall, but only accelerates the fall all the time, so that when the gradient starts to change from a fall to a rise, the local run may be crossedPartial minimum, the Nesterov momentum method, can effectively solve this problem, we first compute J (θ - γ v)t-1) The gradient of (c) predicts the position to be lowered next, and then makes the correction:
experiments show that: the Nesterov momentum method can accelerate the convergence of the RBM model and reduce the oscillation better than the momentum method;
the learning rate is very important in the deep learning process, the error is increased due to too large learning rate, and the RBM model is difficult to fit training data due to the fact that local optimal points are crossed, so that the classification effect is poor; although a learning rate that is too small may avoid these problems, it takes more time to find the local optimum, and in order to reduce the training time and find the local optimum at the same time, an independent adaptive learning rate is used, and for each weight value W, an independent adaptive learning rate is usedijWhen updating, the learning rate is changed in real time by using a parameter α, and the expression is as follows:
wherein h isij(k) Represents the weight W at the time of the k-th trainingijAdaptive coefficient of learning rate, initial hij(0) The setting is 1, and the setting is,represents the weight W at the k-th training after the Nesterov momentum methodijIf the gradient of this training is the same as the gradient of the last training, the corresponding adaptive coefficient is increased α and decreased in acceleration, and conversely, if the gradient of this training is the same as the gradient of the last trainingWhen the signs of the gradients are opposite, the corresponding adaptive coefficients are reduced by 1- α times, and the descent speed is slowed down, the parameter α should be set small, for example, 0.1, so as to ensure that the large adaptive coefficients decay rapidly when oscillations occur, and the adaptive coefficients should be limited to [0.01,100 ] to prevent the gradient from disappearing]。
Further, the step 3 specifically includes the following steps:
step 3.1: deep layer characteristic expression F extracted from DBN models＝{f1,f2,...,fmInputting a Softmax classifier, and training the Softmax classifier alone by combining the label L (as shown in the figure 5):
assuming a total of k classification classes, the system's equation in Softmax regression is:
wherein,represents the classification probability under the k-th probability,are the parameters of the model and are,this term is to normalize the probability distribution such that the sum of all probabilities is 1, here using the signTo represent all model parameters, willArranged in rows to become a k × lsThe matrix of (c) is as follows:
and optimizing the model parameters by adopting a gradient descent method, wherein the cost function in the Softmax regression is as follows:
where 1{ } is an indicative function having a value rule of 1{ an expression whose value is true } -, 1{ } an expression whose value is false } -, 0,and (3) representing a weight attenuation term which penalizes overlarge parameter values, wherein lambda is an attenuation coefficient, and a gradient formula is obtained by derivation:
wherein,is itself a vector whose l-th elementIs thatTo pairPartial derivatives of the l-th component of (a), minimized using a gradient descent algorithmThe parameter updating criterion is as follows:
step 3.2: global fine adjustment based on BP algorithm and conjugate gradient algorithm:
firstly, carrying out forward transmission operation to calculate the activation values of all neurons in the network, and then calculating the residual error of each node i of the l layer "The residual error shows how much the node has influence on the residual error of the final output value, and for the output residual error of the final Softmax regression classification model, the difference is defined as the classification error(the firstnLayer representation output layer), for the hidden unit, a weighted average based on the node residuals is calculatedThe fine tuning method based on the BP algorithm comprises the following steps:
for the lnEach output unit i of a layer (output layer) calculates the residual according to the following formula:
wherein,denotes the lnThe output of the layer node i activates the value,denotes the lnThe input of the layer node i, f' represents the derivative function of the transfer function;
for l ═ ln-1,ln-2,ln3, 2, i-th of the l-th layerThe residual calculation method of each node is as follows:
calculating the partial derivative required by us as follows:
updating the weight parameter:
wherein, Δ W(l)Represents the average gradient of the l-th layer weight W, Δ b(l)And (3) representing the average gradient of the ith layer deviation b, searching the optimal learning rate by using a conjugate gradient method in order to accelerate the descending speed, and repeating the iteration step of the gradient descending method to realize the parameter optimization of the whole DBN model.
Further, the step 4 specifically includes the following steps:
and (3) repeating the step (1) on the unknown state signal to form a test sample set, inputting the test sample set into the trained DBN model to obtain an output value, and judging the state of the equipment according to the output value.
The utility model discloses an application example as follows:
the bearing data of American West university of West storage is taken as an example to explain the deep learning rolling bearing fault diagnosis implementation method based on DBN and Softmax regression. As shown in fig. 9, the rolling bearing test stand comprises a 2-horsepower motor (left side) (1hp 746W), a torque sensor (center), a dynamometer (right side), and electronic control. A single point failure was placed on the support bearing using an electro-discharge machining technique, with failure diameters set at 0.007, 0.014, 0.021, 0.028, 0.040 inches, respectively. The SKF bearings are used for the first three kinds of bearings with fault diameters, and the NTN bearings equivalent to the SKF bearings are used for the last two kinds of bearings with fault diameters. The experiment table comprises a driving end bearing and a fan end bearing, wherein an acceleration sensor is respectively arranged at the driving end of a motor shell and the 12 o' clock position of the fan end. The vibration signals were collected by a 16 channel DAT recorder with a digital signal sampling frequency of 12000 points per second and a drive end bearing failure data sampling rate of 48000 points per second.
In the embodiment of the utility model, we select the vibration signal of Drive End (DE) bearing as original data, divide into bearing trouble four kinds normal, inner circle trouble, rolling element trouble, outer lane trouble. The bearing model is 6205-2RS JEM, the bearing fault size is 0.007 inches, the rotating speed is 1797r/min, the sampling frequency is 12kHz, according to the formula (1), the length of a sample is 400.67, the upper integer is 401, and the specific bearing data can refer to the table 1.
TABLE 1 bearing data
The unsupervised learning process of the laminated limited Boltzmann machine is an important part of fault diagnosis of the rolling bearing, and can effectively learn deep features of original data; after a Nesterov momentum method and an independent self-adaptive learning rate are added, the model training speed can be accelerated, the final fault classification precision is improved, and more representative deep features are extracted from the model; and finally, a Softmax regression algorithm is adopted as a top-layer classifier to realize the classification of the bearing faults.
In this embodiment, the number of hidden layers of the neural network is set to two, and the first layer has 300 neurons. The second layer has 100 neurons and the number of input layer neurons equals the sample length 401. The output layer is set to 4 neurons according to the classified failure mode. The output result of the whole neural network is the matching probability values of different fault modes, the probability of the four fault modes is 1, and the fault mode with the maximum probability is judged to be the fault state of the current bearing.
Inputting a training sample into a DBN model, and training the DBN model and Softmax regression model parameters by adopting the steps 2 and 3, wherein the batch is set to be 80, the initial value of the weight W is 0.01 multiplied by N to (0,1), the initial value of the learning rate is 0.05, the initial values of the deviations a and b of the visible layer and the hidden layer are 0, and the initial value of the learning rate is 0.2. The attenuation coefficient λ is 0.003. After training of all parameters is completed, inputting a test sample can obtain deep features of the sample by using the DBN model through unsupervised self-learning, and then inputting the deep features into the Softmax regression model to realize fault diagnosis of the rolling bearing, and FIG. 5 shows classification results of the test sample. As shown in fig. 6 to 8, in order to better characterize the fault diagnosis result, the model classification result is compared with the real label, the ratio of the number of correctly classified results to the total number of classified results is used as the classification accuracy, the result reaches 98.6%, and table 2 shows the classification accuracy of the specific health state.
TABLE 2 bearing classification results
From analysis application example can see, the utility model provides a deep learning antifriction bearing fault diagnosis method based on DBN and Softmax regression constructs deep belief network model through range upon range of restricted Boltzmann and can learn by oneself effectively and obtain the deep characteristic of antifriction bearing vibration signal, adds Nesterov momentum method and independent self-adaptation learning rate on this basis, can accelerate training speed, improves classification accuracy. The experimental result shows that the bearing fault classification precision reaches 98.6%.
The art related to the present invention is not described in detail.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and it should be noted that, for those skilled in the art, a plurality of modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (4)
1. A self-adaptive deep confidence network bearing fault diagnosis device based on a Nesterov momentum method is characterized in that: comprises that
A signal acquisition module for acquiring raw signals of different health states of the rolling bearing;
the fault diagnosis module is connected with the signal acquisition module and takes the original signal as an input signal to automatically extract deep features of the original signal;
an unknown state signal input module connected with the fault diagnosis module, wherein the fault diagnosis module judges the working state and the fault type of the rolling bearing.
2. The adaptive deep belief network bearing fault diagnosis device based on the Nesterov momentum method as claimed in claim 1, characterized in that: the fault diagnosis module comprises a DBN model building module and a Softmax classifier which are connected, and the DBN model building module is also connected with the signal acquisition module and the unknown state signal input module at the same time.
3. The Nesterov momentum method-based adaptive deep belief network bearing fault diagnosis device of claim 2, characterized in that: the signal acquisition module and the unknown state signal input module are connected with the DBN model building module through a signal classification module.
4. The adaptive deep belief network bearing fault diagnosis apparatus based on the Nesterov momentum method as set forth in any one of claims 1 to 3, characterized in that: the signal acquisition module is a DAS data acquisition system and is used for acquiring vibration signals of the rolling bearings.
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| CN111259834A (en) * | 2020-01-20 | 2020-06-09 | 武汉大学 | Transformer fault diagnosis method and system based on integrated deep belief network |
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