CN108645615A - A kind of Adaptive Fuzzy Neural-network gear method for predicting residual useful life - Google Patents
A kind of Adaptive Fuzzy Neural-network gear method for predicting residual useful life Download PDFInfo
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Abstract
A kind of modified Adaptive Fuzzy Neural-network gear method for predicting residual useful life, belongs to Mechanical Reliability technical field, is characterized in that implementation steps are as follows：1, it is degenerated to gear using vibrating sensor and is monitored in real time；2, feature extraction is carried out to gear fatigue state, slump evaluations is carried out to gear wear degraded performance；3, fuzzy system and neural network are combined, with the deficiency of Neural Network Self-learning mechanism Compensation Fuzzy control system, establishes a kind of fuzzy message fuzzy neural network；4, mnemon is added in all nodes of Fuzzy Processing layer, it by last moment imformation memory and is applied in output this moment, information is made to continue to preserve, reinforce information forward-backward correlation, predicted value and actual value deviation are reduced, modified Adaptive Fuzzy Neural-network forecasting system is established；5, gear remaining life is predicted according to training modified Adaptive Fuzzy Neural-network；Advantage is can effectively to predict gear degenerate state and real-time remaining life, and foundation is provided for gear preventative maintenance.
Description
Technical field
The invention belongs to Mechanical Reliability technical fields, and in particular to a kind of gear method for predicting residual useful life,
Background technology
Gear is the component of machine for transmitting movement and power, and the progressively engaged change campaign of the gear teeth is driven by motor
Size and Orientation in turn hands on the power between roller bearing, is mostly applied in mechanical equipment with closing forms such as gear-boxes, phase
For the transmission mechanism of other forms, gear drive, which is transmitted, has the range of peripheral speed and power wide, efficient, can guarantee perseverance
Fixed transmission ratio, it is safe and reliable the advantages that, nowadays properties of product are continuously improved, and the structure of mechanical equipment system is also exquisite multiple therewith
It is miscellaneous, gear it is easy to appear vibration frequencies when long-term load is operated failures such as high, abrasion or broken teeth, crackle, the study found that more
Several gearbox faults is all caused by gear, and the quality of running state of gear box directly affects the normal fortune of machinery equipment
Make, once equipment part is unable to normal operation, it is possible to and it damages whole equipment or even influences entire production process, cause shutdown etc.
Economic loss even results in catastrophic casualties, therefore, predicting residual useful life is carried out to gear, is to ensure mechanical equipment
The important measures of safe and efficient running and raising product quality.
Invention content
It is an object of the present invention to provide a kind of modified Adaptive Fuzzy Neural-network gear method for predicting residual useful life, this calculations
Method has merged the real-time monitoring information of multi-measuring point, increases mnemon in obscuring on node layer, simultaneously by last moment imformation memory
It is applied in output this moment, is effectively improved the precision of prediction of network model, improved prediction model is with iterations
Increase, error reduces a lot compared to traditional adaptive neural network fuzzy system.
The invention is realized in this way including following implementation steps：
Step 1 installs acceleration transducer on gear-box inner pedestal position, to obtain the real-time of characterization gear condition
Monitoring data, the position mounting temperature sensor right over gear-box install noise transducer in the surface of main examination case；
Step 2 carries out feature extraction to the fatigue state of gear, and slump evaluations are carried out to gear wear degraded performance；
Slump evaluations are carried out to gear wear degraded performance using square amplitude, in each sampling time Δ t/length,
The square amplitude of the time series of discrete random signal is represented by：
Δ t is the sampling time in formula；N=Fs× Δ t, FsFor sample frequency, n is sampling number, and ∑ indicates summation, i ∈
(1,2,3....n), xi(t) it is sampled value；
Step 3 combines fuzzy system and neural network, with the self-study mechanism Compensation Fuzzy control system of neural network
Deficiency, establish a kind of fuzzy neural network of fuzzy message；
Mnemon is added in all nodes of Fuzzy Processing layer in step 4, by last moment imformation memory and is applied to this moment
Output on, make information continue to preserve, reinforce the forward-backward correlation of information, reduce the deviation of predicted value and actual value, establish and improve
Type Adaptive Fuzzy Neural-network, input marking are { x0,x1,L,xt, the output token of hidden layer is { s0,s1,L,st, output mark
It is denoted as { y0,y1,L,yt, information flow, finally to output layer, after mnemon is added, understands guidance information from input layer to hidden layer
It is returned from output unit and hides layer unit, the input of hidden layer not only has this layer input, also includes the state of a upper hidden layer, i.e.,
The node interconnection of hidden layer can also connect certainly, stState, s are walked for the t of hidden layert=f (Uxt+Wst-1), wherein f is activation letter
Number, such as tan or sigmoid functions；U is weights of the input layer to hidden layer, and W is weights of the hidden layer to hidden layer, is calculating s0I.e.
First hiding layer state, needs to use st-1, but be not present, it generally sets to 0 in the implementation；
Choose 4 variable { xt-3r,xt-2r,xt-r,xt, each variable distributes Fuzzy Linguistic Variable, such as big and small, therefore,
16 if-then fuzzy rules are generated, if x1It is Ai l, x2It is Bi l, thenylIt is fuzzy by the l articles
The output of rule is as a result, calculating process is as follows：
1st layer：Input variable is blurred
Xi (i=1,2,3,4.), (j=1,2.) represent input variable,It is arbitrary parametrization membership function, such as
Sigmoid functions：
B, m are premise parameters, its value variation can influence the shape of sigmoid functions,Represent input is subordinate to letter
Numerical value indicates input variable xiIt is under the jurisdiction of AjDegree, be added mnemon after,
It is the weight fed back in the second layer, initial value 0 is continued to optimize in an iterative process,It is one to prolong
Status information of equipment included in last moment data can be remained into subsequent time by slow unit,
2nd layer：Fuzzy operator calculates the relevance grade of each rule
3rd layer：The relevance grade of each rule is normalized
4th layer：Calculate the output of each rule
Wherein { c1,l,c2,l,c3,l,c4,l,c5,lIt is known as consequent parameter,
5th layer：The output of computing system
Then improving the output of Adaptive Fuzzy Neural-network gear predicting residual useful life model is：
Modified Adaptive Fuzzy Neural-network gear predicting residual useful life model corrects each parameter using hybrid algorithm, first
Initial value is assigned to b, m, { c is estimated by least square method1,l,c2,l,c3,l,c4,l,c5,l, finally reversely passed using gradient descent method
Broadcast system error is to correct b, m, and parameter θ is added in prediction model during blurring, and system is in first operation according to upper
The mode of stating corrects each parameter, θ 0；After iteration starts, it is defeated the value that last moment is blurred to be rolled into blurring this moment
In going out, for value near 0.9, using Neural Network Self-learning ability, weights variable is corrected in study from training sample automatically, is adjusted
Whole membership function generates fuzzy rule, by constantly learning to make the response of model constantly to approach reality output；
Step 5 utilizes trained modified Adaptive Fuzzy Neural-network prediction model input test data prediction gear
State, by the degenerate state value predicted and known degenerate state fault threshold can solve for the first time reach fault threshold when
Between.
Advantage of the present invention and good effect：
The present invention proposes a kind of modified Adaptive Fuzzy Neural-network gear method for predicting residual useful life, in fuzzy neural
Mnemon is added in the fuzzy node layer of network, can the facility information for including of last moment be remained into subsequent time and answered
It uses in output, improves the precision of prediction of entire model, apply the invention in the gear predicting residual useful life of gear housing,
Establish prediction model using sample data, be applied in test data show it is good as a result, gear remaining life and each shadow
Inner link and rule between the factor of sound, can make gear remaining life and be effectively predicted, and the present invention proposes modified certainly
Fuzzy neural network gear method for predicting residual useful life is adapted in convergence, error precision, training speed etc. better than tradition
Fuzzy neural network prediction technique.
Description of the drawings
Fig. 1 is the real-time method for predicting residual useful life flow chart of middle gear of the embodiment of the present invention；
Fig. 2 is the prediction of modified Adaptive Fuzzy Neural-network gear predicting residual useful life model in the embodiment of the present invention
Flow chart；
Fig. 3 is modified Adaptive Fuzzy Neural-network gear predicting residual useful life model structure in the embodiment of the present invention
Figure；
Fig. 4 is modified Adaptive Fuzzy Neural-network structure chart in the embodiment of the present invention；
Fig. 5 is the comparison figure of model output of training data and model output in different frequency of training, and Fig. 5 (a) is instruction
When to practice number be 200, the comparison figure of the output of training data and model output, Fig. 5 (b) is frequency of training when being 800, training number
According to output and model export compared with figure, Fig. 5 (c) is frequency of training when being 1000, and output and the model of training data export ratio
Compared with figure；
Fig. 6 is modified Adaptive Fuzzy Neural-network gear predicting residual useful life model in the embodiment of the present invention in difference
When frequency of training, the Error Graph of the output and model output of training data；Fig. 6 (a) be frequency of training be 200 when, training data
Output with model output Error Graph, Fig. 6 (b) be frequency of training be 800 when, training data output with model output error
Figure；When Fig. 6 (c) frequency of training is 1000, the Error Graph of training data output and model output；
Fig. 7 is modified Adaptive Fuzzy Neural-network gear predicting residual useful life model in the embodiment of the present invention in difference
When frequency of training, the comparison figure of test data output and model output；Fig. 7 (a) be frequency of training be 200 when, test data is defeated
Go out the comparison figure exported with model, Fig. 7 (b) is frequency of training when being 800, the comparison figure of test data output and model output；
When Fig. 7 (c) frequency of training is 1000, the comparison figure of test data output and model output；
Fig. 8 is modified Adaptive Fuzzy Neural-network gear predicting residual useful life model in the embodiment of the present invention in difference
When frequency of training, the Error Graph of test data output and model output；Fig. 8 (a) be frequency of training be 200 when, test data is defeated
Go out the Error Graph exported with model, Fig. 8 (b) is frequency of training when being 800, the Error Graph of test data output and model output；
When Fig. 8 (c) frequency of training is 1000, the Error Graph of test data output and model output；
Fig. 9 is that modified Adaptive Fuzzy Neural-network gear predicting residual useful life model intercepts in the embodiment of the present invention
In 200 groups of data, permission precision is 1 × e-4, the comparison figure of test data output and model output；
Figure 10 is that modified Adaptive Fuzzy Neural-network gear predicting residual useful life model intercepts in the embodiment of the present invention
200 groups of data in, permission precision be 1 × e-4, the Error Graph of test data output and model output.
Specific implementation mode
An embodiment of the present invention is described further below in conjunction with the accompanying drawings：
In the embodiment of the present invention, the gear method for predicting residual useful life based on fuzzy neural network, method flow diagram such as Fig. 1
It is shown, include the following steps：
Step 1 carries out fatigue test to gear, obtains the Real-time Monitoring Data that characterization gear is degenerated：
Gear fatigue life experiment uses power flow blocking test rack, and the centre-to-centre spacing of testing stand is 150mm, motor
Rotating speed is 1200r/min, and experiment process is monitored babinet vibration, oil temperature and noise etc., uses material for alloy in experiment
Steel, tooth face hardness are the hardened face gear of 58-61HRC, are surface-treated as carburizing and quenching, and engagement side is staggeredly overlapped using positive and negative
Formula, main examination case module m=3, number of teeth z1=z2=50, pressure angle α=20 °, facewidth 29mm, the real work facewidth 13~
14mm；It is z3=z4=24 to accompany examination case number of gear teeth, and pressure angle α=20 °, work facewidth 20mm, and gear mesh frequency has 2,
Main examination case gear is 1000Hz, and it is 480Hz to accompany examination case gear, and experiment lubricating oil uses L-CKC320 Industrial Closed gear oils；
11 sensors are arranged in experiment altogether, and acceleration transducer is mounted on the bearing block position of main examination case, in gear-box
Mounting temperature sensor installs noise transducer in the surface of main examination case, and No. 1~No. 4 acceleration transducers are arranged in main examination
The radial direction of axle box bearing seat, No. 7 and No. 8 acceleration transducers are arranged in the axial direction of main examination case, No. 5 and No. 6 acceleration transducer cloth
It sets in the radial direction for accompanying examination axle box bearing seat；No. 9 and No. 10 sensors are sonic transducer, be arranged in main examination case and accompanying try case just on
At side about 40cm；No. 11 sensors are the temperature sensors for testing lubricating oil temperature, are arranged in main examination babinet, are tested in experiment
Lubricating oil temperature, sample frequency 25.6kHz, sampling time 60s, sampling interval 9min are using conventional method in groups in this experiment
The mode of dead load carries out, torque 822.7N.M, judges the gear failure when testing gear and broken teeth occurring；
Step 2 carries out feature extraction to the fatigue state of gear, and slump evaluations are carried out to gear wear degraded performance；
Detachment tooth position in gear fatigue test is selected to set recently and in bearing block location arrangements when carrying out life prediction
463 groups of vibration signals of No. 4 sensors output carry out feature extraction, and sample frequency 25.6kHz in experiment, sampling time 60s are adopted
The conversion of sampled point number is monitoring time by sample interval 9min, and the square amplitude curve of No. 4 sensors is after removing individual singular points
Overall variation trend can reflect corresponding with the vibrational energy relationship of experiment each monitoring point abrasion condition of gear, the curve contain from
Start the square amplitude of vibration signal that broken teeth occurs to completion fatigue test at 77.2h for running in stage, by gear fatigue test
Understand square amplitude x when broken teethrms(T*)=77.375 are denoted as degradation, T*To reach the time of fault threshold, T for the first time*=
77.2h, using 4# sensing datas as modified Adaptive Fuzzy Neural-network mode input, square amplitude (Root Mean
Square, RMS) as model output xrms(Δ t) assesses gear degradation characteristics；
Step 3 combines fuzzy system and neural network, with the self-study mechanism Compensation Fuzzy control system of neural network
Deficiency, establish a kind of fuzzy neural network of fuzzy message；
Step 4, modified Adaptive Fuzzy Neural-network gear predicting residual useful life model structure be divided into model training
Stage and model measurement stage were trained training data input prediction model for model training using preceding 200 groups of data
Journey acquires the optimal value of each parameter of network, and 100 groups of data are used for model measurement, and allowable error precision is 1 × e-4Carry out gear fortune
The prediction of row state,
Training learns that the number of membership function value influences training result, and training error can be reduced by increasing number, but can be increased
Calculation amount, this experiment chooses 5 and obtains preferable training result, when being individually below 200,800,1000 for frequency of training, instruction
Practice output and model output (Fig. 5), training data output and model output error (Fig. 6), test data output and the mould of data
Type exports (Fig. 7), test data output and model output error (Fig. 8),
The modified Adaptive Fuzzy Neural-network gear predicting residual useful life model known to Fig. 5,6,7,8 has good
Fitting effect, with the increase of frequency of training, error has good convergence, under parameter the same terms, fuzznet
The error essence of network gear predicting residual useful life model and modified Adaptive Fuzzy Neural-network gear predicting residual useful life model
Degree is as shown in table 1：
12 kinds of model training error precisions of table compare
As seen from the above table, in the case of frequency of training is identical, modified Adaptive Fuzzy Neural-network gear remaining life
Prediction model is lower than the error precision of traditional fuzzy neural network gear predicting residual useful life model, permission error be 1 ×
e-4When, traditional training error needs iteration 864 times, modified Adaptive Fuzzy Neural-network gear predicting residual useful life that need to change
Generation 424 times.
Step 5 carries out gear predicting residual useful life according to gear condition estimation and known gear distress threshold value；
After can be to the monitoring point of system using modified Adaptive Fuzzy Neural-network gear predicting residual useful life model
Degenerate state predicted, can solve and arrive for the first time by the degenerate state value and known degenerate state fault threshold that predict
Up to the time of fault threshold, the gear remaining life as predicted.
In conclusion the present invention proposes modified Adaptive Fuzzy Neural-network gear predicting residual useful life algorithm, in mould
Mnemon is added in paste layer node, can the facility information for including of last moment be remained into subsequent time and be applied to output
On, it is better than traditional fuzzy neural network in convergence, error precision, training speed etc., the remaining longevity in real time can be improved
Order prediction accuracy.
Claims (1)
1. a kind of Adaptive Fuzzy Neural-network gear method for predicting residual useful life, it is characterised in that implementation steps are：
Step 1 installs acceleration transducer in gear-box, obtains the Real-time Monitoring Data of characterization gear condition；
Acceleration transducer is mounted on the bearing block position of main examination case, the mounting temperature sensor in gear-box, in main examination case
Surface is installed by noise transducer；
Step 2 carries out feature extraction to the fatigue state of gear, and slump evaluations are carried out to gear wear degraded performance,
Slump evaluations are carried out to gear wear degraded performance using square amplitude, it is discrete in each sampling time Δ t/length
The square amplitude x of the time series of random signalrms(Δ t) is represented by：
Δ t is using the time in formula；N=Fs× Δ t, FsFor sample frequency, n is sampling number, and ∑ indicates to sum, i ∈ (1,2,
3....n), xi(t) it is sampled value；
Step 3 combines fuzzy system and neural network, not with the self-study mechanism Compensation Fuzzy control system of neural network
Foot, establishes a kind of fuzzy neural network of fuzzy message；
Step 4 establishes a kind of fuzzy god that can handle fuzzy message mnemons is added in all nodes of Fuzzy Processing layer, will
Last moment imformation memory is simultaneously applied in output this moment, is made information continue to preserve, is reinforced the forward-backward correlation of information, establish one
Kind modified Adaptive Fuzzy Neural-network, self-adaptive processing fuzzy message, neural network input marking are { x0,x1,L,xt,
The output token of hidden layer is { s0,s1,L,st, output token is { y0,y1,L,yt, information flow is last from input layer to hidden layer
To output layer, after mnemon is added, meeting guidance information is returned from output unit hides layer unit, and the input of hidden layer not only has
The layer inputs, and also includes the state of a upper hidden layer, i.e. the node interconnection of hidden layer can also connect certainly, stIt is walked for the t of hidden layer
State, st=f (Uxt+Wst-1), wherein f is activation primitive, and U is weights of the input layer to hidden layer, and W is power of the hidden layer to hidden layer
Value is calculating s0That is first hiding layer state, needs to use st-1, but be not present, it sets to 0 in the implementation, the 1st layer：Choose 4
Variable { xt-3r,xt-2r,xt-r,xt, each variable distributes Fuzzy Linguistic Variable, such as big and small, therefore generates 16 if-then moulds
Paste rule, if x1It is Ai l, x2It is Bi l, thenylIt is the output by the l articles fuzzy rule as a result, will
Input variable is blurred,
y1(x1) it is output fuzzy rule, xi(i=1,2,3,4.), (j=1,2.) represent input variable,It is arbitrary ginseng
Numberization membership function is taken as sigmoid functions：
After mnemon is added,
The membership function value of input is represented, indicates input variable xiIt is under the jurisdiction of AjDegree, b, m are premise parameters, it
Value changes the shape that can influence sigmoid functions, θji (2)It is the weight fed back in the second layer, initial value 0, in an iterative process
It continues to optimize,It is a delay cell, status information of equipment included in last moment data can be retained
To subsequent time；
2nd layer：Fuzzy operator calculates the relevance grade of each rule,
3rd layer：The relevance grade of each rule is normalized,
4th layer：The output of each rule is calculated,
Wherein { c1,l,c2,l,c3,l,c4,l,c5,lIt is known as consequent parameter；
5th layer：The output of computing system,
Then modified Adaptive Fuzzy Neural-network prediction model, which exports, is：
Training data input network is corrected into each parameter using hybrid algorithm training network, first gives b, m to assign initial value, by minimum two
Multiplication estimates { c1,l,c2,l,c3,l,c4,l,c5,l, finally changed with correcting b, m using gradient descent method backpropagation systematic error
Be added parameter θ during blurring into type Adaptive Fuzzy Neural-network prediction model, system in first operation according to
Aforesaid way corrects each parameter, θ 0；After iteration starts, the value that last moment is blurred can be rolled into blurring this moment
In output, value is near 0.9, and using Neural Network Self-learning ability, weights variable is corrected in study from training sample automatically,
Membership function is adjusted, fuzzy rule is generated, by constantly learning to make the response of model constantly to approach reality output；
Step 5, using trained modified Adaptive Fuzzy Neural-network prediction model input test data prediction gear condition,
The time for reaching fault threshold for the first time can be solved by the degenerate state value predicted and known degenerate state fault threshold.
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