CN109782603A - The detection method and monitoring system of rotating machinery coupling fault - Google Patents
The detection method and monitoring system of rotating machinery coupling fault Download PDFInfo
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Abstract
The present invention relates to a kind of detection method of rotating machinery coupling fault and monitoring systems, the step of detection method are as follows: the vibration signal data acquired under nominal situation and fault condition using rotating machinery is as training dataset, establish depth convolutional neural networks model, by vibration data directly as input, it introduces and standardizes neuronal activation value from normalization strategy, depth convolutional neural networks model parameter is trained, and save the supplemental characteristic after training, the data under real-time working condition are acquired as test data, pass through depth convolutional neural networks model realization fault detection.The present invention is not necessarily to the accurate mathematical model of industrial process, is convenient for practical application；The class discrimination for realizing fault detection and fault condition simultaneously, can effectively detect the specific component for generating mechanical failure, and Detection accuracy is high.
Description
Technical field
The invention belongs to industrial machinery monitoring and fault diagnosis technology fields, are related to a kind of inspection of rotating machinery coupling fault
Survey method and monitoring system.
Background technique
In modern industrial production, it is examined by the vibration signal of collection machinery component to carry out failure to complex electromechanical systems
Disconnected is one of the diagnostic method being most widely used in current rotary machinery fault diagnosis.Conventional vibration diagnostic method and theory are
Tend to be mature, core is generally divided into two large divisions: first is that the feature extraction of vibration signal, second is that pattern classification.
Rotating machinery, which refers to, relies primarily on the machinery that specific function is completed in spinning movement, is answered extensively in mechanical equipment
With, wherein gear is that the higher critical component of utility ratio, state and geometrical characteristic play the normal operation of mechanical equipment with axis
To vital effect.In mechanical equipment operational process, inevitably there is equipment component aging and wear problem,
The failure mode of resulting gear and bearing is varied, such as: bearing is made of inner ring, outer ring and ball, any bit
Setting generation problem all can lead to bearing fault；The gear distresses such as fracture, hypodontia, abrasion and the scratch of gear.Rotating machinery event
The complexity of barrier is embodied in the diversification of the characteristic polymorphic and failure generational verctor of different fault types, especially work as because
When ageing equipment wear reason makes gear and bearing that coupling fault occur, the feature of vibration signal has more complexity.Pass through event
Characteristic signal is extracted in barrier diagnosis, trouble unit is determined, to avoid bigger loss.
Traditional data-driven fault detection method needs expertise and extensive people using hand-made feature
Power, each module are both needed to gradually train, and can not model to large-scale data, and the accuracy rate of coupling fault detection is poor.
Summary of the invention
There is a problem of fault detection accuracy rate difference when the present invention is for existing fault detection method detection coupling fault, mentions
Detection method and monitoring system for a kind of rotating machinery coupling fault that fault detection accuracy rate is high.
In order to achieve the above object, the present invention provides a kind of detection methods of rotating machinery coupling fault, containing following
Step:
(1) it acquires to be used as under industrial process nominal situation with the multistage sensor measurement data occurred under coupling fault and instruct
Practice data, and establishes training dataset；
(2) depth convolutional neural networks model is established, which is equipped with Floor 12 hidden layer, using five layers of convolutional layer, five
The down-sampled pond layer of layer and two layers of full articulamentum composition model frame, convolutional layer are arranged alternately with down-sampled pond layer, complete to connect
Rear set of the layer in depth convolutional neural networks model；
(3) training dataset is input in depth convolutional neural networks model, neuronal activation value is carried out from normalizing
Change operation, carry out gradient backpropagation training, update each network layer of depth convolutional neural networks model weight matrix parameter and
Lay particular stress on matrix parameter；
(4) the weight parameter matrix of each network layer and weighting parameter square after storage depth convolutional neural networks model training
Battle array；
(5) on-line sensor data are acquired, using depth convolutional neural networks model to the on-line sensor data of acquisition
The affiliated type detection of failure is carried out, faulty generation is judged whether according to the affiliated type of failure.
Further, in step (1), the specific steps of training dataset are established are as follows: under acquisition industrial process nominal situation
With malfunction lower bearing and gear coupling failure time domain vibration signal, including normal condition and 11 kinds of malfunctions totally 12 kinds of shapes
State adds label according to different fault types, and every 2048 sampled points make training dataset as a data sample.
Further, in step (2), in depth convolutional neural networks model, the output of convolutional layer are as follows:
In formula,Indicate the output of convolutional layer,Indicating linear operation, ξ () indicates activation primitive, × indicate convolution behaviour
Make,Indicate that convolution kernel, J indicate the number of convolution kernel, M indicates that the width of convolution kernel, N indicate the length of convolution kernel
Degree,Indicate one layer of convolutional layer output,It indicates to lay particular stress on parameter；
The maximum pond of pond layer is defined as:
In formula,Indicate the pondization output of l i-th of neuron of layer, c indicates pond size；
Full articulamentum is identical structure with the network layer in bp neural network, and the full connection of full articulamentum calculates is defined as:
In formula,Indicate the linear activation of full articulamentum,Indicate the weight parameter matrix of full articulamentum, xl-1In expression
One layer of network layer output.
Further, in step (3), the nerve to each convolutional layer is realized using scaling index linear unit activation function
First activation value is carried out from normalizing operation, the scaling index linear unit activation function representation are as follows:
In formula, λ=1.050700987355480493419, α=1.673263242354377284817.
Further, in step (3), the step of backpropagation training are as follows:
(1) training dataset is input in depth convolutional neural networks model, calculates depth convolutional neural networks model
Target loss function；
(2) gradient is calculated using gradient descent method, adaptability moments estimation algorithm updates weight parameter matrix；
(3) whether training Epoch reaches required value, otherwise the return step (1) if not up to terminates to train, and saves instruction
Weight parameter matrix after white silk.
Further, in step (3), if given fault data collection isWherein xeIt is e-th of data sample, e
=1,2 ..., E indicates number of samples,For the vector of one-hot type, different health are indicated
The label of situation；Classified using softmax separator to coupling fault time domain vibration signal, sample xeNeural network forecast knot
FruitIt indicates are as follows:
In formula,It is the weight matrix of the full articulamentum of most last layer, xe,l-1It is the output matrix of l-1 layers of convolutional layer, blIt indicates
L layers of weighting matrix；
The then target loss function of depth convolutional neural networks model is defined as:
Further, in step (3), adaptability moments estimation algorithm updates the specific steps of weight parameter matrix are as follows:
By learning rate α, single order moments estimation attenuation rate β1, second order moments estimation attenuation rate β2, numerical stability constant ε, depth convolution
Weight parameter matrix θ, target loss function L (θ), frequency of training n, batch size s, the first moment of neural network model are estimated
Meter m and second order moments estimation v is input in depth convolutional neural networks model；
Initialize learning rate α=0.001, single order moments estimation attenuation rate β1It is 0.9, second order moments estimation attenuation rate β2It is 0.99,
Numerical stability constant ε=10-8；Initialize neural network weight parameter matrix θ, use standard deviation for 0.1 random initializtion；
Initialize that single order moments estimation m is 0, second order moments estimation v is 0 simultaneously, frequency of training n is 0；
When not reaching trained termination condition, i.e. target loss function L (θ) is not converged or frequency of training not up to provides
When number:
n←n+1
Gradient descent method calculates gradient,
Update inclined single order moments estimation, m ← β1m+(1-β1)g
Update inclined second order moments estimation, v ← β2v+(1-β2)g⊙g
First moment deviation is calculated,
Second moment deviation is calculated,
Weight is updated,
Reach trained termination condition, returns to weighting parameter θ.
In order to achieve the above object, the present invention also provides a kind of rotating machinery coupling fault detection systems, comprising:
Data acquisition module, for acquire under industrial process nominal situation with occur coupling fault under vibration signal；
The training dataset generation module connecting with data acquisition module, the vibration signal for that will acquire generate training number
According to collection；
The depth convolutional neural networks model being connect with the training dataset generation module, for diagnosing fault；
Model training module, for training depth convolutional neural networks model；
Parameter memory module, for storing the parameter after the depth convolutional neural networks model training；
Breakdown judge module is connect with the data acquisition module and depth convolutional neural networks model, for using deeply
It spends convolutional neural networks model and breakdown judge is carried out to the data of data collecting module collected.
Compared with prior art, the beneficial effects of the present invention are:
The vibration signal data that the present invention is acquired under nominal situation and fault condition using rotating machinery is as training number
According to collection, depth convolutional neural networks model is established, by training dataset directly as input, to depth convolutional neural networks model
Parameter is trained, and saves the supplemental characteristic after training, is acquired the data under real-time working condition as test data, is passed through depth
Convolutional neural networks model realization fault detection.The present invention is not necessarily to the accurate mathematical model of industrial process, is convenient for practical application；Together
When realize the class discrimination of fault detection and fault condition, can effectively detect the specific component for generating mechanical failure, inspection
It is high to survey accuracy rate.
Detailed description of the invention
Fig. 1 is the flow chart of the detection method of rotating machinery coupling fault of the present invention；
Fig. 2 is the framework of depth convolutional neural networks model in the detection method of rotating machinery coupling fault of the present invention
Figure；
Fig. 3 is the training flow chart of depth convolutional neural networks model in rotating machinery coupling fault of the present invention；
Fig. 4 is that 12 kinds of different fault types vibrate letter in the detection method of rotating machinery coupling fault of the present invention
Number time-domain diagram；
Fig. 5 is that the test error of the embodiment of the present invention one loses curve graph；
Fig. 6 is the failure detection result confusion matrix figure of the embodiment of the present invention one；
Fig. 7 is the structure diagram that rotating machinery coupling fault of the present invention monitors system.
Specific embodiment
In the following, the present invention is specifically described by illustrative embodiment.It should be appreciated, however, that not into one
In the case where step narration, element, structure and features in an embodiment can also be advantageously incorporated into other embodiments
In.
Referring to Fig. 1, present invention discloses a kind of detection method of rotating machinery coupling fault, this method directly reads sensing
The original time domain vibration signal acquired on device, the depth convolutional neural networks model after training being capable of real time on-line monitoring machine
The health status of rotating machinery in tool system excavates high-level abstract characterization, obtains accurate fault diagnosis result, contain
Following steps:
It is used as and instructs with the multistage sensor measurement data occurred under coupling fault under S101, acquisition industrial process nominal situation
Practice data, and establishes training dataset；
S102, establish depth convolutional neural networks model, which is equipped with Floor 12 hidden layer, using five layers of convolutional layer,
Five layers of down-sampled pond layer and two layers of full articulamentum composition model frame, convolutional layer are arranged alternately with down-sampled pond layer, Quan Lian
Layer is connect in the rear set of depth convolutional neural networks model；
S103, training dataset is input in depth convolutional neural networks model, neuronal activation value return certainly
One changes operation, carries out gradient backpropagation training, updates the weight matrix parameter of each network layer of depth convolutional neural networks model
With weighting matrix parameter；
The weight parameter matrix of each network layer and weighting parameter square after S104, storage depth convolutional neural networks model training
Battle array；
S105, acquisition on-line sensor data, using depth convolutional neural networks model to the on-line sensor number of acquisition
According to the affiliated type detection of failure is carried out, faulty generation is judged whether according to the affiliated type of failure.
In the above-mentioned detection method of the present invention, the specific steps of training dataset are established are as follows: acquisition industrial process nominal situation
Lower and malfunction lower bearing and gear coupling failure time domain vibration signal, including normal condition and totally 12 kinds of 11 kinds of malfunctions
State adds label according to different fault types, and every 2048 sampled points make training data as a data sample
Collection.
Convolutional neural networks imply in convolutional neural networks generally by this up of three-layer of input layer, hidden layer and output layer
Layer generally comprises the composition such as convolutional layer, pond layer and full articulamentum, and multilayer convolution replaces with pond layer, constitutes one completely
Depth convolutional neural networks model.Convolutional layer can carry out weight to weight parameter w and share when carrying out convolution operation, can
Reduce the memory headroom of the calculation amount and occupancy during model training.
Usual convolutional neural networks are widely used in computer vision field, are suitable for recognition of face, digital handwriting body
The Classification and Identification of the two dimensional images such as identification, the input of model are generally two dimension input.The present invention shakes for the time domain of rotating machinery
Dynamic signal carries out fault diagnosis, since time domain vibration signal is one-dimensional data, needs two-dimensional convolution operations improvement to be one-dimensional volume
Product operation, to adapt to the input of original vibration signal.
In the depth convolutional neural networks model constructed in the above-mentioned detection method of the present invention, referring to fig. 2, which includes defeated
Enter layer, hidden layer and output layer, wherein hidden layer is Floor 12, using five layers of convolutional layer, five layers of down-sampled pond layer and two layers
Full articulamentum composition model frame, convolutional layer are arranged alternately with down-sampled pond layer, and full articulamentum is in depth convolutional neural networks
The rear set of model.Specifically, referring to table 1, the framework of depth convolutional neural networks model hidden layer are as follows: the convolution being sequentially connected
Layer C1, pond layer P2, convolutional layer C3, pond layer P4, convolutional layer C5, pond layer P6, convolutional layer C7, pond layer P8, convolutional layer C9,
Pond layer P10, full articulamentum F11 and full articulamentum F12.Data processing and calculating are executed by node between layers.
Table 1
| Network layer | Core size | Nuclear volume | Output size |
| C1 | 64×1 | 16 | 512×16 |
| P2 | 2×1 | 16 | 256×16 |
| C3 | 3×1 | 32 | 256×32 |
| P4 | 2×1 | 32 | 128×32 |
| C5 | 3×1 | 64 | 128×64 |
| P6 | 2×1 | 64 | 64×64 |
| C7 | 3×1 | 64 | 64×64 |
| P8 | 2×1 | 64 | 32×64 |
| C9 | 3×1 | 64 | 32×64 |
| P10 | 2×1 | 64 | 16×64 |
| F11 | 100 | 1 | 100×1 |
| F12 | 12 | 1 | 12×1 |
To adapt to rotating machinery periodic vibration signal, other convolutional layers are different from, the convolution kernel of convolutional layer C1 uses 64
× 1 big convolution kernel, convenient for extracting feature；The convolution kernel of convolutional layer C3, C5, C7 and C9 are 3 × 1 greatly, and depth is respectively
16,32,64,64；Pond layer P2, P4, P6, P8 and P10 are using maximum pondization operation, and P2 layers are 4 × 4, remaining is 2 × 2
Structure；It is 12 × 1 that the output of full articulamentum F11, which is 100 × 1, F12 layers, eventually passes through softmax classifier and is divided into 12 classes.Most
Eventually by the vector for the one-hot types that the final output of convolutional neural networks is 12 dimensions, if it is that vibration signal is corresponding
A kind of failure, then output is [0,1,0,0,0,0,0,0,0,0,0,0], if corresponding second of failure, output for [0,0,1,0,
0,0,0,0,0,0,0,0], if there is no failure, equipment normal operation exports [1,0,0,0,0,0,0,0,0,0,0,0].
In convolutional layer, convolution kernel is a series of parameter matrixs for capableing of shared parameter, and one group of connection can be shared same
Weight, rather than each it is connected with a different weight.The output of convolutional layer are as follows:
In formula,Indicate the output of convolutional layer,Indicating linear operation, ξ () indicates activation primitive, × indicate convolution behaviour
Make,Indicate that convolution kernel, J indicate the number of convolution kernel, M indicates that the width of convolution kernel, N indicate the length of convolution kernel
Degree,Indicate one layer of convolutional layer output,It indicates to lay particular stress on parameter.
In convolutional layer output, the effect of activation primitive is that linear operation is carried out to non-linear words, enhances fitting effect.Needle
It is difficult to the feature extracted to rotating machinery coupling fault, using normalization strategy, the neuronal activation value of each convolutional layer is carried out
From normalizing operation, is realized using scaling index linear unit (referred to as: SELUs) activation primitive and the neuron of each convolutional layer is swashed
Value living is carried out from normalizing operation, the scaling index linear unit activation function representation are as follows:
In formula, λ=1.050700987355480493419, α=1.673263242354377284817.
It carries out avoiding extracting height from normalizing operation using scaling index linear unit (referred to as: SELUs) activation primitive
Gradient suddenly disappears or the problem of explosive increase when dimensional feature.
The above-mentioned detection method of the present invention, selection increase pond layer after convolutional layer and carry out pondization operation, mainly carry out
Down-sampling is further reduced number of parameters by removing unessential sample in the characteristic information obtained after convolution.In the present invention
It states in detection method, carries out the method for down-sampling using maximum pond method, the maximum pond of pond layer is defined as:
In formula,Indicate the pondization output of l i-th of neuron of layer, c indicates pond size.
The above-mentioned detection method of the present invention, full articulamentum add the rear set in entire depth convolutional neural networks model, entirely
Articulamentum is identical structure, the mark of the Feature Mapping for mainly arriving e-learning to sample with the network layer in bp neural network
Remember in space, the two dimensional character figure that convolutional layer exports is converted into an one-dimensional vector, is convenient for subsequent failure modes.Quan Lian
The full connection for connecing layer calculates is defined as:
In formula,Indicate the linear activation of full articulamentum,Indicate the weight parameter matrix of full articulamentum, xl-1In expression
One layer of network layer output.
Referring to Fig. 3, in above-mentioned detection method, the step of depth convolutional neural networks model backpropagation training are as follows:
(1) training dataset is input in depth convolutional neural networks model, calculates depth convolutional neural networks model
Target loss function；
(2) gradient is calculated using gradient descent method, adaptability moments estimation algorithm updates weight parameter matrix；
(3) whether training Epoch reaches required value, otherwise the return step (1) if not up to terminates to train, and saves instruction
Weight parameter matrix after white silk.
Specifically, if given fault data collection isWherein xeIt is e-th of data sample, e=1,2 ..., E table
Show number of samples,For the vector of one-hot type, the label of different health status is indicated；
Classified using softmax separator to coupling fault time domain vibration signal, sample xeNeural network forecast resultIt indicates are as follows:
In formula,It is the weight matrix of the full articulamentum of most last layer, xe,l-1It is the output matrix of l-1 layers of convolutional layer, blIt indicates
L layers of weighting matrix；
The then target loss function of depth convolutional neural networks model is defined as:
Depth convolutional neural networks model optimizes above-mentioned target loss function using gradient descent method, is updated with this
Weight parameter matrix and weighting parameter matrix.
In the above-mentioned detection method of the present invention, the depth convolutional neural networks model of use, since the number of plies is 12 layers, the number of plies compared with
It is deep, training data is based on using adaptability moments estimation algorithm and iteratively updates neural network weight parameter matrix.Specifically, it adapts to
Property moments estimation algorithm update weight parameter matrix specific steps are as follows:
By learning rate α, single order moments estimation attenuation rate β1, second order moments estimation attenuation rate β2, numerical stability constant ε, depth convolution
Weight parameter matrix θ, target loss function L (θ), frequency of training n, batch size s, the first moment of neural network model are estimated
Meter m and second order moments estimation v is input in depth convolutional neural networks model；
Initialize learning rate α=0.001, single order moments estimation attenuation rate β1It is 0.9, second order moments estimation attenuation rate β2It is 0.99,
Numerical stability constant ε=10-8；Initialize neural network weight parameter matrix θ, use standard deviation for 0.1 random initializtion；
Initialize that single order moments estimation m is 0, second order moments estimation v is 0 simultaneously, frequency of training n is 0；
When not reaching trained termination condition, i.e. target loss function L (θ) is not converged or frequency of training not up to provides
When number:
n←n+1
Gradient descent method calculates gradient,
Update inclined single order moments estimation, m ← β1m+(1-β1)g
Update inclined second order moments estimation, v ← β2v+(1-β2)g⊙g
First moment deviation is calculated,
Second moment deviation is calculated,
Weight is updated,
Reach trained termination condition, returns to weighting parameter θ.
The above-mentioned detection method of the present invention, the vibration signal number acquired under nominal situation and fault condition using rotating machinery
According to as training dataset, depth convolutional neural networks model is established, by training dataset directly as input, to depth convolution
Neural network model parameter is trained, and saves the supplemental characteristic after training, acquires the data under real-time working condition as test
Data pass through depth convolutional neural networks model realization fault detection.The above-mentioned detection method of the present invention is accurate without industrial process
Mathematical model, be convenient for practical application；The class discrimination for realizing fault detection and fault condition simultaneously, can effectively detect
The specific component of mechanical failure is generated, Detection accuracy is high.
Referring to Fig. 7, the present invention also provides a kind of rotating machinery coupling fault detection systems, comprising:
Data acquisition module 1, for acquire under industrial process nominal situation with occur coupling fault under vibration signal；
The training dataset generation module 2 connecting with data acquisition module 1, the vibration signal for that will acquire generate training
Data set；
The depth convolutional neural networks model 3 being connect with the training dataset generation module 2, for diagnosing fault；
Model training module 4, for training depth convolutional neural networks model；
Parameter memory module 5, for storing the parameter after the depth convolutional neural networks model training；
Breakdown judge module 6 is connect, for utilizing with the data acquisition module 1 and depth convolutional neural networks model 3
Depth convolutional neural networks model carries out breakdown judge to the data that data acquisition module 1 acquires.
The above-mentioned rotating machinery coupling fault monitoring system of the present invention passes through in data collecting module collected machine driven system
The coupled vibrations sensing data of parallel-shaft gearbox middle (center) bearing and gear, as on-line testing data, the test data and mould
Training dataset data in type training module are consistent.Can detecte using the above-mentioned monitoring system of the present invention recognize it is trained
Gear-bearing coupling fault, and detect the affiliated type of the failure, judge whether faulty generation, fault detection accuracy rate
Height, it is practical.
In order to verify the detection method of the above-mentioned rotating machinery coupling fault of the present invention to the effective of coupling rotating machinery fault
Property, and the above-mentioned rotating machinery coupling fault monitoring system of the verifying present invention, its progress is distinguished below with two specific embodiments
Explanation.
Embodiment one: wind turbine power transmission fault diagnosis comprehensive experiment table is used, the acceleration on experimental bench is passed through
The bearing and gear coupling fault vibration signal of sensor acquisition parallel-shaft gearbox are spent, acquires 11 kinds of fault conditions and normal altogether
Mechanical oscillation signal under operating condition makes training dataset and test data set.
The vibration signal of acquisition totally 12 kinds of different fault types, referring to table 2.As shown in Table 2,12 kinds of fault type difference
Are as follows: (1) normal condition N, (2) inner-ring bearing failure IF, (3) roller bearing fault condition RF, (4) race bearing failure OF, (5)
Coupling fault IRO occurs for inner ring, ball and outer ring condition, and coupling event occurs for (6) ball bearing and cut-off wheel state (RCH)
Coupling fault, (8) race bearing and teeth-missing gear state (OCH) occur for barrier, (7) ball bearing and Gear with Crack state (RCR)
Shaft coupling failure occurs, coupling fault, (10) inner-ring bearing and hypodontia occur for (9) race bearing and Gear with Crack state (OCR)
Shaft coupling failure occurs for gear condition (ICH), and coupling fault, (12) occur for (11) inner-ring bearing and Gear with Crack state (ICR)
In ball bearing, crackle and cut-off wheel state coupling fault RCC.The time domain vibration signal of every kind of failure is referring to fig. 4.
Table 2
In order to prove the present invention using depth convolutional neural networks model on rotating machinery coupling fault test problems
Validity, the present invention by with BP neural network, SCNN (activation primitive be sigmoid function) and sparse self-encoding encoder come pair
Than verifying.
Setting learning rate is 0.001, dropout 0.5, and every 2048 sampled points are as a data sample, using 696
A data sample is trained, and experiment carries out 1500 training in total.Depth convolutional neural networks model uses 12 layer networks altogether
The structure of layer, using small batch training.
It is tested by training, by the loss function curve of test process as shown in figure 5, curve has had reached convergence shape
State.Comparative experiments classifies situation to the identification of 12 kinds of different faults, the standard of the rotating machinery coupling fault diagnosis under algorithms of different
True rate is referring to table 4.As shown in Table 4, the present invention has reached 95.8% using the correct recognition rata of depth convolutional neural networks model,
It is compared to the 60.3% of traditional BP neural network, the 81.5% of 76.2% and SCNN of sparse self-encoding encoder, is promoted respectively
30.3%, 19.4% and 8.1%.
Table 4
| Algorithm | Sample type | Sample size | Accuracy rate |
| ANN | Time-domain signal | 2048×1 | 60.3% |
| Sparse self-encoding encoder | Time-domain signal | 2048×1 | 76.2% |
| SCNN(Sigmoid) | Time-domain signal | 2048×1 | 81.5% |
| DCNN | Time-domain signal | 2048×1 | 95.8% |
In order to which the classification results of every kind of health status are described in detail, the confusion matrix of the measuring accuracy of the experiment is drawn, is mixed
Matrix confuse referring to Fig. 6.It will be appreciated from fig. 6 that single failure is easier identified and can obtain higher standard compared to coupling fault
True rate, close to 100%, and the coupling fault classification accuracy of bearing and gear is slightly worse, has certain probability to assign to and is coupled
Single failure in, but still ensure that 90% or more accuracy rate.
Embodiment two: in order to verify rotating machinery coupling fault monitoring system of the present invention to the reality that can satisfy on-line monitoring
Time when monitoring system diagnostic data and the relationship of batch size are tested in the requirement of when property.By being surveyed in difference batch size
It tries, the time required for available proposed one signal of system diagnostics is about 44ms, used wind turbine mechanomotive force
The sample frequency that drive failures diagnose the acceleration transducer of collection machinery vibration signal in comprehensive experiment table is 5.12KMz, often
A sample signal takes 2048 sampled points, and the relationship of time and batch size is referring to table 5, as shown in Table 5, rotating machinery coupling of the present invention
Closing fault monitoring system can be very good the requirement for meeting real-time.
Table 5
| Criticize size | 11 | 23 | 35 | 47 | 59 | 71 | 83 |
| Time (s) | 0.0439 | 0.0437 | 0.0423 | 0.0445 | 0.0451 | 0.0438 | 0.0427 |
Embodiment provided above only with illustrating the present invention for convenience, and it is not intended to limit the protection scope of the present invention,
Technical solution scope of the present invention, person of ordinary skill in the field make various simple deformations and modification, should all include
In the above claim.
Claims (8)
1. a kind of detection method of rotating machinery coupling fault, which is characterized in that contain following steps:
(1) it acquires and is used as training number with the multistage sensor measurement data occurred under coupling fault under industrial process nominal situation
According to, and establish training dataset；
(2) depth convolutional neural networks model is established, which is equipped with Floor 12 hidden layer, using five layers of convolutional layer, five layers of drop
Sampling pool layer and two layers of full articulamentum composition model frame, convolutional layer are arranged alternately with down-sampled pond layer, and full articulamentum exists
The rear set of depth convolutional neural networks model；
(3) training dataset is input in depth convolutional neural networks model, neuronal activation value grasp from normalization
Make, carries out gradient backpropagation training, update the weight matrix parameter and weighting of each network layer of depth convolutional neural networks model
Matrix parameter；
(4) the weight parameter matrix of each network layer and weighting parameter matrix after storage depth convolutional neural networks model training；
(5) on-line sensor data are acquired, are carried out using on-line sensor data of the depth convolutional neural networks model to acquisition
The affiliated type detection of failure judges whether faulty generation according to the affiliated type of failure.
2. the detection method of rotating machinery coupling fault as described in claim 1, which is characterized in that in step (1), establish
The specific steps of training dataset are as follows: when under acquisition industrial process nominal situation with malfunction lower bearing and gear coupling failure
Domain vibration signal, including normal condition and 11 kinds of malfunctions totally 12 kinds of states, add label according to different fault types, often
2048 sampled points make training dataset as a data sample.
3. the detection method of rotating machinery coupling fault as claimed in claim 2, which is characterized in that in step (2), in depth
It spends in convolutional neural networks model, the output of convolutional layer are as follows:
In formula,Indicate the output of convolutional layer,Indicating linear operation, ξ () indicates activation primitive, × indicate convolution operation,Indicating that convolution kernel, J indicate the number of convolution kernel, M indicates that the width of convolution kernel, N indicate the length of convolution kernel,Indicate one layer of convolutional layer output,It indicates to lay particular stress on parameter；
The maximum pond of pond layer is defined as:
In formula,Indicate the pondization output of l i-th of neuron of layer, c indicates pond size；
Full articulamentum is identical structure with the network layer in bp neural network, and the full connection of full articulamentum calculates is defined as:
In formula,Indicate the linear activation of full articulamentum,Indicate the weight parameter matrix of full articulamentum, xl-1Indicate one layer
Network layer output.
4. the detection method of rotating machinery coupling fault as claimed in claim 3, which is characterized in that in step (3), use
The realization of index linear unit activation function is scaled to carry out from normalizing operation, the scaling neuronal activation value of each convolutional layer
Index linear unit activation function representation are as follows:
In formula, λ=1.050700987355480493419, α=1.673263242354377284817.
5. the detection method of rotating machinery coupling fault as described in claim 3 or 4, which is characterized in that in step (3), instead
To the step for propagating training are as follows:
(1) training dataset is input in depth convolutional neural networks model, calculates the target of depth convolutional neural networks model
Loss function；
(2) gradient is calculated using gradient descent method, adaptability moments estimation algorithm updates weight parameter matrix；
(3) whether training Epoch reaches required value, and otherwise the return step (1) if not up to terminates to train, after saving training
Weight parameter matrix.
6. the detection method of rotating machinery coupling fault as claimed in claim 5, which is characterized in that in step (3), if giving
Determining fault data collection isWherein xeIt is e-th of data sample, e=1,2 ..., E indicate number of samples, For the vector of one-hot type, the label of different health status is indicated；Using softmax separator pair
Coupling fault time domain vibration signal is classified, sample xeNeural network forecast resultIt indicates are as follows:
In formula,It is the weight matrix of the full articulamentum of most last layer, xe,l-1It is the output matrix of l-1 layers of convolutional layer, blIndicate l layers
Weighting matrix；
The then target loss function of depth convolutional neural networks model is defined as:
7. the detection method of rotating machinery coupling fault as claimed in claim 5, which is characterized in that in step (3), adapt to
Property moments estimation algorithm update weight parameter matrix specific steps are as follows:
By learning rate α, single order moments estimation attenuation rate β1, second order moments estimation attenuation rate β2, numerical stability constant ε, depth convolutional Neural
The weight parameter matrix θ of network model, target loss function L (θ), frequency of training n, batch sizes, single order moments estimation m with
And second order moments estimation v is input in depth convolutional neural networks model；
Initialize learning rate α=0.001, single order moments estimation attenuation rate β1It is 0.9, second order moments estimation attenuation rate β2It is 0.99, numerical value
Stability constant ε=10-8；Initialize neural network weight parameter matrix θ, use standard deviation for 0.1 random initializtion；Simultaneously
Initialization single order moments estimation m is 0, second order moments estimation v is 0, and frequency of training n is 0；
When not reaching trained termination condition, i.e., target loss function L (θ) is not converged or frequency of training is not up to stipulated number
When: n ← n+1
Gradient descent method calculates gradient,
Update inclined single order moments estimation, m ← β1m+(1-β1)g
Update inclined second order moments estimation, v ← β2v+(1-β2)g⊙g
First moment deviation is calculated,
Second moment deviation is calculated,
Weight is updated,
Reach trained termination condition, returns to weighting parameter θ.
8. a kind of rotating machinery coupling fault monitors system characterized by comprising
Data acquisition module, for acquire under industrial process nominal situation with occur coupling fault under vibration signal；
The training dataset generation module connecting with data acquisition module, the vibration signal for that will acquire generate training data
Collection；
The depth convolutional neural networks model being connect with the training dataset generation module, for diagnosing fault；
Model training module, for training depth convolutional neural networks model；
Parameter memory module, for storing the parameter after the depth convolutional neural networks model training；
Breakdown judge module is connect with the data acquisition module and depth convolutional neural networks model, for being rolled up using depth
Product neural network model carries out breakdown judge to the data of data collecting module collected.
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