CN108108832A - A kind of oil truck oil and gas leakage intelligent monitor system based on wireless sensor network - Google Patents
A kind of oil truck oil and gas leakage intelligent monitor system based on wireless sensor network Download PDFInfo
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Abstract
The invention discloses a kind of oil truck oil and gas leakage intelligent monitor systems based on wireless sensor network, oil truck is detected in way parameter for realization and oil truck oil and gas leakage concentration intelligent predicting, the system are formed by being gathered based on the oil truck of wireless sensor network in way state parameter with intelligent predicting platform and oil truck oil and gas leakage concentration intelligent forecast model two parts；It is an object of the present invention to provide a kind of oil truck oil and gas leakage intelligent monitor system based on wireless sensor network, which monitors humiture of the oil truck in way is travelled, pressure and the security information such as whether leaks in real time, so as to avoid the generation problem of contingency.
Description
Technical field
The present invention relates to special transport vehicle testing techniques of equipment fields, and in particular to one kind is based on wireless sensor network
Oil truck oil and gas leakage intelligent monitor system.
Background technology
China oil field many places are in remote districts, therefore petroleum transportation becomes an important step in petroleum industry.Oil is transported
Defeated to have long-distance and two kinds of short distance, long-distance transport relies primarily on oil pipeline, for short distance and it is some do not have along railwayed, stone
The outward transport work of oil relies primarily on oil truck.Oil truck accumulating because its it is motor-driven and flexible the features such as, become short distance oil product transport
Main carriers.Oil is flammability hazard article, and driver generally does not allow to stop privately during transport, unless running into road
Situations such as obstruction, vehicle trouble, is forced to stop, and especially can not arbitrarily be stopped in city prosperity location, so as to avoid an accident thing
Therefore.It is devised to monitor humiture of the oil truck in way is travelled, pressure in real time and security information, the application such as whether leak
Perceive oil tank locomotive safety information wireless sensor network node, construct it is a kind of based on the oil truck of wireless sensor network way
Condition monitoring system.The system based on the oil truck of wireless sensor network in way state parameter by being gathered and intelligent predicting platform
Formed with oil truck oil and gas leakage concentration intelligent forecast model, realize accurate monitoring to oil truck oil and gas leakage concentration with it is pre-
It surveys, good impetus is played to improving oil truck long-distance transport effect.
The content of the invention
It, should it is an object of the present invention to provide a kind of oil truck oil and gas leakage intelligent monitor system based on wireless sensor network
System monitors humiture of the oil truck in way is travelled, pressure and the security information such as whether leaks in real time, so as to avoid unexpected thing
Therefore generation problem.
The invention is realized by the following technical scheme：
A kind of oil truck oil and gas leakage intelligent monitor system based on wireless sensor network is realized and oil truck is joined on way
Number is detected and oil truck oil and gas leakage concentration intelligent predicting, it is characterised in that：The intelligent monitor system is included based on nothing
The oil truck of line sensor network is in the acquisition of way state parameter and intelligent predicting platform, oil truck oil and gas leakage concentration intelligent predicting
Model；
It is described to be gathered based on the oil truck of wireless sensor network in way state parameter with intelligent predicting platform by multiple inspections
Node and on-site supervision end composition are surveyed, and is built into oil truck in an ad-hoc fashion and is put down in the acquisition of way state parameter with intelligent predicting
Platform；The detection node is made of sensor group module, microcontroller and wireless communication module NRF2401, is responsible for detection oil truck
The actual value of the temperature on surface, pressure and gas concentration；It realizes and oil truck is managed in way parameter in the on-site supervision end
It is predicted with to oil truck in way leakage gas concentration；
The oil truck oil and gas leakage concentration intelligent forecast model include multiple NARX neural network prediction models, Fuzzy C-
Means clustering algorithm grader, multiple empirical mode decomposition models, multigroup ANFIS Network Prediction Models and the ELman based on PSO
Network oil truck oil and gas leakage concentration Fusion Model；Each NARX neural network prediction models output is as Fuzzy C-Means Clustering
The input of algorithm classification device, Fuzzy C-Means Clustering Algorithm grader carry out multiple NARX neural network prediction models output valves
Classification, each type of NARX neural network prediction models export the input as each empirical mode decomposition model, Mei Gejing
Test input of the multiple outputs of mode decomposition model as every group of ANFIS Network Prediction Model, every group of ANFIS Network Prediction Model
Input of the fusion forecasting value as the ELman network oil truck oil and gas leakage concentration Fusion Models based on PSO, based on PSO's
The output valve of ELman network oil truck oil and gas leakage concentration Fusion Models leaks gas concentration value for oil truck.
Further Technological improvement plan is the present invention：
The multiple NARX neural network prediction models carry out the oil and gas leakage concentration of each test point in oil truck surface
Prediction, the inputs of multiple NARX neural network prediction models are each test point gas concentration value, Fuzzy C-Means Clustering Algorithm
Grader exports multiple NARX neural network prediction models according to the characteristic value that multiple NARX neural network prediction models export
Value is classified.
Further Technological improvement plan is the present invention：
The output valve of each type NARX neural network prediction models is defeated as each empirical mode decomposition model
Enter, the output valve of each type NARX neural network prediction models is decomposed into low frequency trend portion by each empirical mode decomposition model
Point and multiple high-frequency fluctuation parts, low frequency trend part and multiple high-frequency fluctuation parts are respectively as every group of ANFIS neural network forecast
The input of model, the output equal weight of every group of each ANFIS Network Prediction Model mutually sum it up to obtain every group of ANFIS neural network forecast mould
The fusion forecasting value of type.
Further Technological improvement plan is the present invention：
The fusion forecasting value of every group of ANFIS Network Prediction Model is as the ELman network oil truck oil gas based on PSO
The input of concentration Fusion Model is leaked, the ELman network oil truck oil and gas leakage concentration Fusion Model based on PSO is realized to multigroup
The fusion of the fusion forecasting value of ANFIS Network Prediction Models leaks gas concentration value as oil truck.
Compared with prior art, the present invention with following obvious advantage：
First, the present invention uses the input of NARX neural network prediction models to include one section of oil truck oil and gas leakage concentration
Time outputs and inputs historical feedback, and the input of this partial feedback is it is considered that contain the oil truck oil and gas leakage of a period of time
CONCENTRATION STATE historical information participates in the prediction of oil truck oil and gas leakage concentration, for a suitable feedback time length, prediction
Good effect is obtained, the NARX neural network prediction patterns of this patent provide a kind of effective prediction oil truck oil gas and let out
Leak concentration method.
2nd, NARX neural network prediction models of the present invention are that one kind can be effectively dense to oil truck oil and gas leakage
The dynamic neural network model that non-linear, the nonstationary time series of degree are predicted, can be in the non-stationary drop of time series
The precision of prediction to oil truck oil and gas leakage concentration-time sequence is improved in the case of low.With traditional prediction model method phase
Than the method has the advantages of processing nonstationary time series effect is good, and calculating speed is fast, and accuracy rate is high.By to non-stationary
Oil truck oil and gas leakage concentration experiment data actual comparison, this patent demonstrates NARX neural network prediction models to oil tank
The feasibility of vehicle oil and gas leakage concentration-time sequence prediction.Meanwhile experimental result also demonstrates NARX neural network prediction models
It is more excellent than conventional model performance in non-stable time series forecasting.
3rd, the present invention is introduced using NARX neural network oil truck oil and gas leakage concentration prediction models due to passing through
Time delay module and output feedback establish the dynamic recurrent neural network of model, it will output and input vectorial Time-delayed Feedback and introduce network instruction
In white silk, new input vector is formed, there is good non-linear mapping capability, the input of network model not only includes being originally inputted
Data, also comprising the output data after training, the generalization ability of network is improved, and makes it in non-linear oil truck oil gas
Leaking static neural network more traditional in concentration-time sequence prediction has better precision of prediction and adaptive ability.
4th, the present invention is exported original deformation NARX neural network prediction models by empirical mode decomposition model (EMD)
Sequence is decomposed into the component of different frequency range, each component shows the different characteristic information lain in former sequence.With drop
Low sequence it is non-stationary.High frequency section data correlation is not strong, and frequency is higher, represents the ripple components of original series, tool
There are certain periodicity and randomness, this is consistent with the cyclically-varying of oil truck oil and gas leakage concentration；Low-frequency component represents
The variation tendency of former sequence.It can be seen that EMD can decomposite step by step the ripple components of oil truck oil and gas leakage concentration, periodic component and
Trend components, each component decomposited itself include identical deformation information, reduce different characteristic to a certain extent
Interfering between information, and the original oil truck oil and gas leakage concentration Deformation Series of each component variation curve ratio decomposited are bent
Linear light is slided.It can be seen that EMD can effectively analyze the oil truck oil and gas leakage concentration deformation data under multifactor collective effect, decomposition obtains
Each component be conducive to the foundation of ANFIS Network Prediction Models and be better anticipated.ANFIS nets are established respectively using to each component
Network prediction model, for extreme learning machine is avoided to input random the problems such as being lost with component information that dimension is chosen, first to each point
Phase space reconstruction is measured, is finally superimposed each component prediction result to obtain final fusion forecasting result.Case study shows what is carried
Fusion forecasting result has higher precision of prediction.
5th, the present invention builds fuzzy C-mean algorithm according to the characteristics of oil truck oil and gas leakage concentration prediction parameter differences between samples
Cluster (FCM) grader classify to oil truck oil and gas leakage concentration multiple spot forecast sample parameter, design multiple EMD models and
Multigroup ANFIS Network Prediction Models predict the sample parameter of oil truck oil and gas leakage concentration prediction again respectively, in oil tank
Among vehicle oil and gas leakage concentration prediction continuous and discrete process, take into full account oil truck oil and gas leakage concentration when space characteristic,
Similar in the origin cause of formation, the data of relative homogeneous are extracted from the data of magnanimity grade, and to establish, specific aim is stronger, can more react
Random time stage oil truck oil and gas leakage concentration prediction model improves precision of prediction.
6th, multigroup ANFIS Network Prediction Models proposed by the present invention are a kind of fuzzy based on Takagi-Sugeno models
Inference system is the new fuzzy inference system structure by fuzzy logic and neuroid combination, is passed using reversed
Algorithm and the hybrid algorithm adjustment premise parameter and consequent parameter of least square method are broadcast, and automatically generates If-Then rules.It is multigroup
ANFIS Network Prediction Models as a kind of very distinctive neutral net, it is similary have with arbitrary accuracy approach it is arbitrary linear and
The function of nonlinear function, and fast convergence rate, sample requirement are few.Model calculation speed is fast, and reliable results have obtained effect
Fruit.
7th, ELman networks oil truck oil and gas leakage concentration Fusion Model of the present invention, the Elman god of the model
4 layers are generally divided into through network:Input layer, interlayer (hidden layer), undertaking layer and output layer, input layer, hidden layer and output
The connection of layer is similar to feedforward network, and the unit of input layer only plays signal transmission, the linear weighting effect of output layer unit.
Linearly or nonlinearly function can be used in the transmission function of implicit layer unit, accepts layer and is also known as context level or state layer, it is used
Remember the output valve of implicit layer unit previous moment, may be considered the operator that is once delayed.Elman type neutral nets
Feature be the output of hidden layer by accepting the delay and storage of layer, be linked to the input of hidden layer certainly, it is this to make it from connection mode
There is sensibility to the data of historic state, the addition of internal feedback network adds the energy that network basis is in reason multidate information
Power, so as to achieve the purpose that dynamic modeling.The characteristics of ELMAN type recurrent nerve metanetworks is that the output of hidden layer passes through structure list
Delay, the storage of member are linked to the input of hidden layer certainly, this from connection mode it to be made to have sensibility to the data of historic state, interior
The addition of portion's feedback network adds the ability that network basis is in reason multidate information, is conducive to the modeling of dynamic process；The model
Using the feedback link of associated layers dynamic neuron, the information of future anticipation network and past prediction network is merged, is made
Network strengthens to the memory of time series characteristic information, so as to improve oil truck oil and gas leakage concentration prediction precision.
Description of the drawings
Fig. 1 be the present invention is based on wireless sensor network oil truck way state parameter acquisition with intelligent predicting platform,
1- detection nodes, 2- on-site supervisions end；
Fig. 2 is oil truck oil and gas leakage concentration intelligent forecast model of the present invention；
Fig. 3 is detection node functional diagram of the present invention；
Fig. 4 is on-site supervision end of the present invention software function diagram；
Fig. 5 is the ELman network oil truck oil and gas leakage concentration Fusion Models the present invention is based on PSO；
Fig. 6 is one group of ANFIS Network Prediction Model of the invention；
Fig. 7 is oil truck of the present invention in the acquisition of way state parameter and intelligent predicting platform plane layout drawing.
Specific embodiment
With reference to attached drawing 1-7, technical solution of the present invention is further described：
First, the design of system general function
A kind of oil truck oil and gas leakage intelligent monitor system based on wireless sensor network of Patent design of the present invention, it is real
Now oil truck is detected in way surface temperature, pressure and leakage oil gas parameter and in way oil truck oil and gas leakage concentration intelligence
Prediction, the system based on the oil truck of wireless sensor network in way state parameter by being gathered and intelligent predicting platform and oil truck
Oil and gas leakage concentration intelligent forecast model two parts form.It is gathered based on the oil truck of wireless sensor network in way state parameter
Include detection node 1 and on-site supervision end 2 with intelligent predicting platform, they are built into wireless test and control network in an ad-hoc fashion
Realize the wireless communication between detection node 1 and on-site supervision end 2；Detection node 1 is by the oil truck of detection in way state parameter
It is sent to on-site supervision end 2 and preliminary treatment is carried out to sensing data；On-site supervision end 2 shows oil truck in way status predication
State parameter.Whole system structure is as shown in Figure 1.
2nd, the design of detection node
Using largely detection node 1 based on wireless sensor network as oil truck in way state parameter perception terminal,
Multiple detection nodes 1 and on-site supervision end 2 are made up of the information interact system of wireless sensor network self-organizing.Detection
Node 1 includes acquisition oil truck surface temperature, pressure, the sensor of oil gas and the micro- place of corresponding signal conditioning circuit, MSP430
Manage device and NRF2401 wireless transport modules；The software of detection node mainly realizes wireless communication and oil truck in way conditions Ambient
The acquisition and pretreatment of parameter.Software is designed using C programmer, and degree of compatibility is high, substantially increases Software for Design exploitation
Work efficiency enhances the reliability, readability and portability of program code.Detection node structure is as shown in Figure 3.
3rd, on-site supervision end software
On-site supervision end 2 is an industrial control computer, and on-site supervision end 2, which is mainly realized, joins oil truck in way state
Number is acquired and oil truck oil and gas leakage concentration intelligent forecast model, realization and the information exchange of detection node 1, on-site supervision
2 major functions are held as messaging parameter setting, data analysis and data management and oil truck oil and gas leakage concentration intelligent forecast model.
The management software has selected Microsoft Visual++6.0, and as developing instrument, the Mscomm communication controls of calling system come
Communication program is designed, on-site supervision end software function is as shown in Figure 4.Oil truck oil and gas leakage concentration intelligent forecast model designs
Following steps：
1st, NARX neural network prediction models design
NARX neural network prediction models such as Fig. 2, NARX neutral nets (Nonlinear Auto-Regression with
External input neural network) it is a kind of dynamic feedforward neural network, NARX neutral nets, which are one, to be had
The nonlinear auto-companding network of oil truck oil and gas leakage concentration input, the dynamic characteristic of multi-step delay there are one it, and pass through anti-
If the dried layer of feedback connection close network, NARX recurrent neural networks are most widely used a kind of dynamics in nonlinear dynamic system
Neutral net, performance are generally better than total regression neutral net.One typical NARX recurrent neural networks mainly by input layer,
It hidden layer, output layer and outputs and inputs delay and forms, the delay exponent number, hidden output and input to be generally determined in advance before application
Layer neuron number, the output at that time of NARX neural network prediction models depend not only on past output y (t-n), also depend on
In delay exponent number of input vector X (t) at that time and input vector etc..NARX neural network prediction models structure includes input
Layer, output layer, hidden layer and time delay layer.Wherein oil truck oil and gas leakage concentration passes to hidden layer by time delay layer, and hidden layer is to input
Signal handled after be transferred to output layer, hidden layer output signal is done linear weighted function and obtains final neutral net by output layer
Prediction output signal, the signal that time delay layer exports the signal of network-feedback and input layer are then delivered to hidden layer into line delay.
NARX neutral nets have the characteristics that non-linear mapping capability, good robustness and adaptivity, suitable for oil truck oil gas
Leakage concentration is predicted.X (t) represents the external input of neutral net, i.e., the oil truck oil and gas leakage concentration of multiple test points
Value；M represents externally input delay exponent number；Y (t) is the output of neutral net, i.e. the oil truck oil and gas leakage of subsequent period is dense
Spend predicted value；N is output delay exponent number；S is the number of hidden layer neuron；It is hereby achieved that j-th implies the defeated of unit
Go out for：
In above formula, wjiFor the connection weight between i-th of input and j-th of hidden neuron, bjIt is j-th of implicit nerve
The bias of member, the value of the output y (t+1) of network are：
Y (t+1)=f [y (t), y (t-1) ..., y (t-n), x (t), x (t-1) ..., x (t-m+1)；W] (2)
2nd, cluster (FCM) classifier design of fuzzy C-mean algorithm
If finite aggregate X={ x1,x2,…xnIt is the set that n sample forms, they are each NARX neutral nets respectively
The maximum value average value and minimum of prediction model, C be predetermined classification number, mi(i=1,2 ... c) it is each cluster
Center, μj(xi) it is degree of membership of i-th of sample on jth class, clustering criteria function is defined as by membership function:
In formula:||xi-mj| | it is xiTo mjBetween Euclidean distance；B is FUZZY WEIGHTED power exponent, is that can control cluster
As a result the parameter of fog-level；M is the Fuzzy C Matrix dividing of X, and V is the cluster centre set of X, and the result of FCM clustering algorithms is just
It is to obtain the M and V that criterion function is made to reach minimum.In fuzzy C-means clustering method, it is desired to which sample is to the person in servitude of each cluster
The sum of category degree is 1, i.e.,:
The minimum of formula (3) is sought under conditions of formula (4), makes J (M, V) to mjAnd μj(xi) partial derivative be 0, can obtain minimum
The necessary condition of value is:
FCM algorithms can be completed according to following iterative step:
A, clusters number c and parameter b is set, algorithm terminates threshold epsilon, iterations t=1, and permission greatest iteration number is
tmax；
B, each cluster centre m is initializedi；
C, membership function is calculated according to formula (4) with current cluster centre；
D, with current membership function all kinds of cluster centres are updated by formula (3)；
E, suitable matrix norm is chosen, if | | V (t+1)-V (t) | |≤ε or t >=tmax, stop computing；Otherwise, t
=t+1, return to step C.
When algorithmic statement, all kinds of cluster centres and each NARX neural network prediction models output valve sample pair are obtained
In all kinds of degrees of membership, fuzzy clustering division is completed.Fuzzy clustering result is finally subjected to de-fuzzy, fuzzy clustering is changed
Classify for certainty, realize final NARX neural network prediction models output valve classification, as shown in Figure 2.
3rd, empirical mode decomposition modelling
Empirical mode decomposition (EMD) is a kind of self-adapting signal screening technique, have calculate it is simple, directly perceived, based on experience
And the characteristics of adaptive.The trend step-sizing that it can will be present in different characteristic in signal comes out, and obtains multiple high-frequency fluctuations
Partly (IMF) and low frequency trend part.EMD decomposite come IMF components contain signal from high to low different frequency sections into
Point, the frequency resolution that each frequency band includes changes in itself with signal, has adaptive multiresolution analysis characteristic.It uses
The purpose that EMD is decomposed is exactly to more accurately extract fault message.IMF components must simultaneously meet two conditions：1. it is treating
In decomposed signal, the number of extreme point it is equal with the number of zero crossing or at most difference one；2. on any time, by office
The envelope average that portion's maximum and local minimum define is zero.Empirical mode decomposition method is directed to NARX neural network prediction moulds
" screening " process steps of type output value signal are as follows：
(1) all Local Extremums of NARX neural network prediction models output value signal are determined, then use cubic spline
The Local modulus maxima of left and right is connected to form coenvelope line by line.
(2) local minizing point of NARX neural network prediction model output valves is being connected into shape with cubic spline line
Into lower envelope line, upper and lower envelope should all data points of envelope.
(3) average value of upper and lower envelope is denoted as m1(t), it is obtained：
x(t)-m1(t)=h1(t) (7)
X (t) is NARX neural network prediction model output valve original signals, if h1(t) it is an IMF, then h1(t)
It is exactly first IMF component of x (t).Remember c1(t)=h1k(t), then c1(t) first for signal x (t) meets IMF conditions
Component.
(4) by c1(t) separate, obtain from x (t)：
r1(t)=x (t)-c1(t) (8)
By r1(t) step (1)-step (3) is repeated as initial data, obtains the 2nd point for meeting IMF conditions of x (t)
Measure c2.Repetitive cycling n times obtain the n components for meeting IMF conditions of signal x (t).So pass through empirical mode decomposition model
The output of NARX neural network prediction models is decomposed into low frequency trend part and multiple high-frequency fluctuation parts, experience decomposition model
As shown in Figure 2.
4th, multigroup ANFIS Network Prediction Models design
Since fuzzy reasoning does not possess self-learning function in itself, application is greatly limited, and artificial neural network
Fuzzy language cannot be expressed again, actually similar to a black box, lacks transparency, so the reasoning of human brain cannot be expressed well
Function.Adaptive nuero-fuzzy inference system system ANFIS based on neutral net, also referred to as Adaptive Neuro-fuzzy Inference
(Adaptive Neuro-Fuzzy Inference System), the two is organically combined, and can play the excellent of the two
Point, and respective deficiency can be made up.Fuzzy membership function and fuzzy rule in adaptive network-based fuzzy system are logical
Cross what the study to a large amount of given datas obtained, the characteristics of ANFIS is maximum is namely based on the modeling method of data rather than is based on
Experience or intuition are any given.The system that this is not understood also completely for those characteristics by people or characteristic is extremely complex
It is particularly important.The input of ANFIS Network Prediction Models is respectively each multiple high-frequency fluctuation parts (IMF) and low frequency trend
Partial maximum value average value and minimum, the major calculations step of ANFIS Network Prediction Models are as follows:
1st layer:By the data obfuscation of input, each node corresponds to output and is represented by:
Patent of the present invention is 3 nodes, is the pole of each multiple high-frequency fluctuation parts (IMF) and low frequency trend part respectively
Big value, average value and minimum.Formula n is each input membership function number, and membership function uses Gauss member function.
2nd layer:Implementation rule computing exports the relevance grade of rule, and the regular computing of ANFIS Network Prediction Models, which uses, to be multiplied
Method.
3rd layer:The relevance grade of each rule is normalized:
4th layer:The transmission function of each node is linear function, represents local linear model, each adaptive node i
It exports and is:
5th layer:The single node of this layer is a stationary nodes, and the output for calculating ANFIS Network Prediction Models is:
The conditional parameter and the consequent parameter of inference rule that membership function shape is determined in ANFIS Network Prediction Models can be with
It is trained by learning process.Parameter declines the algorithm combined adjustment with gradient using Linear least square estimation algorithm and joins
Number.ANFIS Network Prediction Models each time transfer along network is positive until the 4th layer first in iteration by input signal, fixes at this time
Conditional parameter adjusts consequent parameter using least-squares estimation algorithm；Signal continue along network it is positive transfer until output layer (i.e.
5th layer).ANFIS Network Prediction Models by the error signal of acquisition along network backpropagation, with gradient method update condition parameter.
The conditional parameter given in ANFIS Network Prediction Models is adjusted by this method, can with it is concluded that parameter it is global most
Advantage so can not only reduce the dimension of search space in gradient method, can also improve ANFIS Network Prediction Model parameters
Convergence rate.One group of ANFIS Network Prediction Model, which is realized, predicts a kind of NARX neural network prediction models output valve, one
Output low frequency part and multiple high-frequency fluctuation part of the input of group ANFIS Network Prediction Models for empirical mode decomposition model,
Each ANFIS Network Prediction Models output equal weight mutually sums it up to obtain one group of ANFIS network in one group of ANFIS Network Prediction Model
The fusion forecasting value of prediction model is as shown in Figure 6.
5th, the ELman network oil truck oil and gas leakage concentration Fusion Model design based on PSO
ELman network oil truck oil and gas leakage concentration Fusion Models based on PSO are as shown in figure 5, Elman networks can be seen
Work is a feedforward neural network with local mnemon and local feedback link.In addition to hidden layer, there are one especially
Associated layers.The layer receives feedback signal from hidden layer, and there are one corresponding association node layer companies for each hidden node
It connects.Associated layers by the hidden layer state of last moment together with current time network inputs together as the input of hidden layer, be equivalent to
Feedback of status.The transmission function of hidden layer is generally Sigmoid functions, and output layer is linear function, and associated layers are also linear function.
In order to efficiently solve the approximation accuracy problem in fault diagnosis, enhance the effect of associated layers, propose a kind of ELman networks oil tank
Vehicle oil and gas leakage concentration Fusion Model is as shown in Figure 5.Z-1Represent step delay operator.If the input layer of network, output layer, hidden layer
Number be respectively m, n and r；w1, w2, w3And w4Represent structure layer unit to hidden layer, input layer to hidden layer, hidden layer to defeated respectively
Go out layer, the connection weight matrix of structure sheaf to output layer, then the expression formula of network is:
cp(k)=xp(k-1) (15)
If the preferable output of network is yt, reality output y, error function is：
ELman network oil truck oil and gas leakage concentration Fusion Models based on PSO are as shown in Figure 5.The model includes Elman
Neural network structure determines, PSO algorithm optimizations and Elman neural network prediction three parts.Basic step is as follows：
Step1, the M groups input of given neutral net, output sample are standardized as training set, and by initial data；
Step2, Elman neural network structures are determined according to input, output parameter number, so that it is determined that PSO algorithm particles
Length；
Step3, by all interneuronal indirect weights in Elman network structures, threshold coding into real number representation
Individual.It is if each individual by the N-dimensional being made of N number of weights, threshold parameter vector comprising N number of optimization weights, threshold value in network
To represent initialization population；
Step4, to predict Error Absolute Value and as ideal adaptation angle value, and according to fitness value obtain individual extreme value and
Global extremum；
Step5, judge whether global extremum meets PSO termination conditions, if satisfied, exiting PSO optimizing, go to Step6；If
It is unsatisfactory for, updates each particle rapidity and position, go to Step4；
Particle corresponding to Step6, decoding global extremum, and initial weight in this, as Elman networks, threshold value；
Step7, the optimal initial weight and threshold value that are obtained in Step6 are assigned to Elman networks, training simultaneously determines network mould
Type predicts load with trained neural network model.
4th, oil truck is in the acquisition of way state parameter and intelligent predicting platform plane layout drawing
Detection node, prison are placed in the upper surface of oil truck oil tank, lower surface and front and back sides according to the structure of oil truck
Surveying oil truck, whether leakage situation occurs for pot temperature, pressure and gas in way is travelled.System arranges multiple detection nodes 1
With the horizontal layout installation diagram at on-site supervision end 2, wherein 1 equilibrium of detection node is arranged in the upper following table of detected oil truck oil tank
Face and front and back sides, whole system horizontal layout are shown in Fig. 7, wherein upper and lower surface layout layout with front and back sides it is similar, in figure with
Explained exemplified by layouting above, by the system realize to oil truck way parameter acquisition with to way oil and gas leakage concentration into
Row prediction.
The technical means disclosed in the embodiments of the present invention is not limited only to the technological means disclosed in the above embodiment, further includes
Formed technical solution is combined by more than technical characteristic.It should be pointed out that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (4)
1. a kind of oil truck oil and gas leakage intelligent monitor system based on wireless sensor network, is realized to oil truck in way parameter
It is detected and oil truck oil and gas leakage concentration intelligent predicting, it is characterised in that：The intelligent monitor system is included based on wireless
The oil truck of sensor network is in the acquisition of way state parameter and intelligent predicting platform, oil truck oil and gas leakage concentration intelligent predicting mould
Type；
It is described to be saved based on the oil truck of wireless sensor network in the acquisition of way state parameter with intelligent predicting platform by multiple detections
Point and on-site supervision end composition, and oil truck is built into an ad-hoc fashion in the acquisition of way state parameter and intelligent predicting platform；
The detection node is made of sensor group module, microcontroller and wireless communication module NRF2401, is responsible for detection oil truck surface
Temperature, the actual value of pressure and gas concentration；The on-site supervision end, which is realized, is managed and right oil truck in way parameter
Oil truck is predicted in way leakage gas concentration；
The oil truck oil and gas leakage concentration intelligent forecast model includes multiple NARX neural network prediction models, Fuzzy C-means
Clustering algorithm grader, multiple empirical mode decomposition models, multigroup ANFIS Network Prediction Models and the ELman networks based on PSO
Oil truck oil and gas leakage concentration Fusion Model；Each NARX neural network prediction models output is as Fuzzy C-Means Clustering Algorithm
The input of grader, Fuzzy C-Means Clustering Algorithm grader divide multiple NARX neural network prediction models output valves
Class, each type of NARX neural network prediction models export the input as each empirical mode decomposition model, each experience
Input of the multiple outputs of mode decomposition model as every group of ANFIS Network Prediction Model, every group of ANFIS Network Prediction Model
Input of the fusion forecasting value as the ELman network oil truck oil and gas leakage concentration Fusion Models based on PSO, based on PSO's
The output valve of ELman network oil truck oil and gas leakage concentration Fusion Models leaks gas concentration value for oil truck.
2. a kind of oil truck oil and gas leakage intelligent monitor system based on wireless sensor network according to claim 1,
It is characterized in that：The multiple NARX neural network prediction models to the oil and gas leakage concentration of each test point in oil truck surface into
Row prediction, the input of multiple NARX neural network prediction models is each test point gas concentration value, and Fuzzy C-Means Clustering is calculated
Method grader is defeated to multiple NARX neural network prediction models according to the characteristic value that multiple NARX neural network prediction models export
Go out value to classify.
3. a kind of oil truck oil and gas leakage intellectual monitoring system based on wireless sensor network according to claim 1 or 2
System, it is characterised in that：The output valve of each type NARX neural network prediction models is as each empirical mode decomposition mould
The output valve of each type NARX neural network prediction models is decomposed into low frequency by the input of type, each empirical mode decomposition model
Trend part and multiple high-frequency fluctuation parts, low frequency trend part and multiple high-frequency fluctuation parts are respectively as every group of ANFIS net
The input of network prediction model, the output equal weight of every group of each ANFIS Network Prediction Model mutually sum it up to obtain every group of ANFIS network
The fusion forecasting value of prediction model.
4. a kind of oil truck oil and gas leakage intellectual monitoring system based on wireless sensor network according to claim 1 or 2
System, it is characterised in that：The fusion forecasting value of every group of ANFIS Network Prediction Model is as the ELman network oil tanks based on PSO
The input of vehicle oil and gas leakage concentration Fusion Model, the ELman network oil truck oil and gas leakage concentration Fusion Model based on PSO are realized
Gas concentration value is leaked as oil truck to the fusion of the fusion forecasting value of multigroup ANFIS Network Prediction Models.
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