CN102169555A - Relevance sensing signal multiple sensing element fault location and sensing signal self-recovery method - Google Patents
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Abstract
本发明公开了一种关联性传感信号多传感元件故障定位及传感信号自恢复方法，该方法包括：获取训练样本；建立符合多路传感元件信号关联性的神经网络，并学习传感信号之间的关联性；利用学习的各信号关联性关系，将含故障传感元件的传感信号输入神经网络，根据网络输出信号与输入信号之间的差值，对输入信号进行故障判断；定位出多路故障传感元件；恢复多路故障传感信号。该方法针对传感信号之间具有关联性的情况，并不需要求解关联函数，而是通过自动调节神经网络隐含层节点函数的权重，从而学习并利用其关联性，方法具有简便性、快捷性。
The invention discloses a multi-sensing signal fault location and sensing signal self-recovery method. The correlation between the sensing signals; using the learned correlation of each signal, input the sensing signal containing the fault sensing element into the neural network, and judge the fault of the input signal according to the difference between the network output signal and the input signal ; Locate the multi-path fault sensing element; restore the multi-path fault sensing signal. This method does not need to solve the correlation function for the correlation between the sensor signals, but learns and utilizes the correlation by automatically adjusting the weight of the node function of the hidden layer of the neural network. The method is simple and fast sex.
Description
Technical field
The present invention relates to many sensing elements localization of fault and transducing signal self-recovery method, relate in particular to a kind of at many sensing elements localization of fault that has relevance between the transducing signal and transducing signal self-recovery method.
Background technology
Many complex processes in the objective world need to handle simultaneously the sensing system from the multichannel transducing signal, and this promotion can connect the appearance of the integrated intelligent mode sensor of many sensing elements.Every road sensing element that these novel integrated sensors connect all can walk abreast and extract the correlated characteristic information of detected object separately, and communicates information in the sensor.Typical many sensing elements sensor has the networked smart sensor based on the IEEE1451 standard, and it can connect 255 road sensing elements at most simultaneously measures, and has standardization, characteristics that integrated level is high.
Connect the multichannel sensing element simultaneously and brought facility for the detection of many sensor source signals, but brought some problems simultaneously yet: break down as, the sensing element (one or more) that connects, detection signal is not followed when measured, is difficult to the fault location element; Under some occasion, each transducing signal of sensor-based system has relevance, and each road transducing signal all can impact other road transducing signal, still fails to utilize the relevance that has between each road transducing signal, reconstruct fault sensing element signal value makes transducing signal from recovering.
Summary of the invention
For solving above-mentioned middle problem and the defective that exists, the invention provides a kind of relevance transducing signal many sensing elements localization of fault and transducing signal self-recovery method.Described technical scheme is as follows:
Relevance transducing signal many sensing elements localization of fault and transducing signal self-recovery method comprise:
A, obtain training sample;
B, foundation meet the neural network of multichannel sensing element signal association, and the relevance between the study transducing signal;
C, utilize each signal association sexual intercourse of study, will contain the transducing signal input neural network of fault sensing element,, input signal is carried out fault judgement according to the difference between network output signal and the input signal;
D, orient multichannel fault sensing element;
E, recovery multichannel fault transducing signal.
The beneficial effect of technical scheme provided by the invention is:
Broken through and to have solved the thinking set that the relation of relevance between the transducing signal just can be carried out localization of fault, utilize to innovation its relevance, by the value of exploratory change multichannel transducing signal within the specific limits, thereby realize that the location of multichannel fault sensing element and signal are from recovering.
Description of drawings
Fig. 1 is the process flow diagram of multichannel sensing element localization of fault and transducing signal self-recovery method;
Fig. 2 is the structural representation of the neural network of structure;
Fig. 3 is parallel 9 fork tree fault location algorithm process flow diagrams;
Fig. 4 is expansion 9 fork tree signal reconstruction algorithm flow charts;
Fig. 5 is a kind of phthalic anhydride still testing and control project figure;
Fig. 6 is the neural metwork training procedure chart.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, embodiment of the present invention is described further in detail below in conjunction with accompanying drawing:
Present embodiment provides a kind of relevance transducing signal many sensing elements localization of fault and transducing signal self-recovery method.
Referring to Fig. 1, this method may further comprise the steps:
The above-mentioned training sample that obtains comprises abundant training sample.
The neural network of above-mentioned foundation is the neural network that meets multichannel sensing element signal association.
The above-mentioned transducing signal input neural network that will contain the fault sensing element can carry out fault judgement to input signal according to the difference between network output signal and the input signal.
Above-mentioned is the value that changes multichannel sensing element signal in fixed range, makes it meet all each road transducing signal x
iBetween relevance relation, i=1 wherein, 2 ..., n, and orient multichannel fault sensing element according to the difference change conditions of network output signal and input signal.
According to transducing signal relevance relation, continue to change the signal of fault location sensing element, make the output signal of neural network and input signal difference less than setting threshold, thereby recover the fault transducing signal automatically.
Above-mentioned steps 10 specifically comprises with step 20: as shown in Figure 2, at 10 tunnel relevance transducing signals of phthalic anhydride still, make up the neural network of a 10-13-5-13-10 structure.With the input layer of 10 road transducing signals formation neural network under the abundant normal operating conditions, relevance relation between 10 road transducing signals under the training place normal production conditions.Because the existence of noise, the condition that network training finishes is not that SSE minimizes, but converges to a preset threshold Δ when it
SSEJust stop training, otherwise network will attempt noise is learnt, thereby reduce the neural network generalization ability.In this network, Δ
SSEBe set at 0.0001, through 1.793 seconds, the network training success, its Fig. 6 is the neural metwork training procedure chart.
Above-mentioned steps 30 specifically comprises: the measuring-signal { x that obtains the multichannel sensing element
i| i=1,2 ..., 10} is with transducing signal X=[x
1, x
2..., x
10]
TIn the input neural network, observe network output Y=[y
1, y
2..., y
10]
TAnd the SSE between the input signal judges whether sensing element exists fault, if SSE≤Δ
SSE, the sensing element non-fault; If SSE＞Δ
SSE, one or more sensing element non-fault need enter step 40 and 50 and carry out localization of fault and signal recovery certainly.
Above-mentioned steps 40 and step 50 specifically comprise: to judging the signal that has the fault sensing element, to the tree-shaped fault search that walks abreast of each road signal, draw SSE and reduce the most tangible one group, thereby orient the fault sensing element within the specific limits; After orienting the fault sensing element, the tree-shaped signal reconstruction algorithm of proceeding to expand makes SSE≤Δ
SSEThereby, realize that transducing signal is from recovering.
The technological core of present embodiment is: make up a feed-forward type transmission network with symmetrical topological structure, this neural network unique distinction is to have unity gain, that is, and and its input vector x under the normal condition
i(i=1,2 ..., n) equal output vector y
i(i=1,2 ..., n).Figure 2 shows that the structural drawing of the neural network of structure, comprise an input layer, three hidden layers and an output layer.The hidden layer ground floor is a mapping layer, and node dimension p is maximum in whole network, is used for extracting input information relevance part, and its node transport function can be Sigmoid function (f (x)=1/ (1+e
-x)) or other similar nonlinear functions, the weight of this node layer function is w
C-j(j=1,2 ..., p, p＞n); The last one deck of hidden layer is for separating mapping layer, and the node dimension equates with mapping layer, is used for information is carried out the relevance reduction, and its node is a nonlinear transfer function, and the weight of this node layer function is w
D-j(j=1,2 ..., p, p＞n).
Above-mentioned neural network can be trained with error back propagation (BP) algorithm.Training sample is the measurement data of respectively organizing sensing element under the unfaulty conditions, and by enough training samples, neural network can be by automatically adjusting the weight w of each node transport function of hidden layer (comprise mapping layer, bottleneck layer conciliate mapping layer), makes x
1=f
In-1(x
2, x
3..., x
n)=f
Out-1(y
2, y
3... y
n)=y
1, x
2=f
In-2(x
1, x
3..., x
n)=f
Out-2(y
2, y
3..., y
n)=y
2..., x
n=f
In-n(x
1, x
2..., x
N-1)=f
Out-n(y
1, y
2..., y
N-1)=y
2Thereby, learn each input signal relevance relation each other.At this moment, can utilize each signal association sexual intercourse of study, input signal is carried out fault judgement.If trouble-free sensing data is input in the neural network network output vector y
i(i=1,2 ..., n) with input vector x
i(i=1,2 ..., n) certainly will equate; Because the weight w of each node transport function is constant, if wherein one the tunnel or the multichannel sensing element break down output quantity y so
iTo all change corresponding input quantity x
iWith output vector y
iTo there are differences comprehensively.Therefore, definition neural network output vector y
iWith input vector x
iBetween error sum of squares SSE, its formula is as follows:
Index is passed judgment on the factor as the sensing element fault.
If each the road sensing element that connects all is in the non-fault perfect condition, obvious SSE=0; Because each node transport function weight w of neural network in the network training stage, is determined by the associate feature of each road transducing signal, if input vector X
iIn one or more sensing element break down output vector Y
iTo all change.The thinking of multichannel fault sensing element method for searching then is based on the associate feature of transducing signal so, by each road fault element signal of exploratory change within the specific limits (increase, reduce or constant), makes at this moment output vector Y
iWith input vector X
iReach unanimity.Is how many road sensing elements the difficult point that this method exists: 1. have break down actually? it specifically is again which road? be 2. how many reconstruction value of each road generation sensing element fault-signal? could guarantee output vector Y
iWith input vector X
iReach unanimity.Solve this two problems, the location that will realize the fault sensing element; Another one is to realize the recovery certainly of transducing signal.
Be example with IEEE 1451 intelligence sensors that connected the relevant sensing elements of 10 road signals below, the embodiment how present embodiment is overcome the above problems is described in further detail: 10 road transducing signals of establishing intelligence sensor are { x
i| i=1,2 ..., 10} then can utilize each transducing signal X=[x
1, x
2..., x
10]
TBe the input node of neural network, when having one or more sensing element fault, obvious SSE ≠ 0.
When supposing only to have one road sensing element fault, can exploratoryly within the specific limits give smaller value δ of each road sensing element signal change successively
S, the numerical value X ' after will changing again
1=[x
1+ δ
S, x
2..., x
10]
T, X '
2=[x
1, x
2+ δ
S..., x
10]
T... X '
n=[x
1, x
2..., x
10+ δ
S]
TInput neural network is observed the variation of SSE this moment.
From formula 4-formula 8 as can be known, if the input signal after certain group changes can make SSE significantly diminish, illustrate that the transducing signal X ' after changing meets the incidence relation of training signal, thereby judge that this road sensing element breaks down; Behind the fault location sensing element, continue to change this road signal value, make SSE less than the setting threshold Δ
SSE, then can recover this road transducing signal.
If exploratory change one road sensing element signal within the specific limits, SSE can't obtain significantly and reduce, more than one road sensing element fault is described, need considers to change simultaneously the multichannel transducing signal, below emphasis the situation that two-way fault sensing element location is arranged in 10 road transducing signals down simultaneously is described in detail in detail.At this moment, may exist the sensing element combination of fault to have
Kind, table 1 has been listed the combination of all two-way sensing element simultaneous faultss, these be combined into line ordering be respectively 1,2}, 1,3} ..., 9,10}.
Exploratory within the specific limits successively to the smaller value of sensing element signal change in each combination, if the combination of the sensing element in the test is the fault combination, according to the transducing signal incidence relation, change the input value of this two-way sensing element within the specific limits, corresponding SSE will reduce; If instead the test combination is trouble-free, from above-mentioned formula (1) as can be seen, therefore the value that changes them only can cause SSE further to enlarge, and by changing the input value of every combination, obtains SSE and reduces the most significant combination and can locate impaired sensing element.
Suppose j (j≤10) number and k (k≤10, number sensing element fault of k ≠ j) then artificially respectively changes a little value δ to them
jAnd δ
k, neural network input this moment node will become (referring to formula 2):
X＝[x
1，x
2，...，x
j+δ
1，...，x
k+δ
k，...，x
10]
T (2)
The signal of considering a fault sensing element has three kinds of different {+δ of variation
i,-δ
i, 0} (wherein " 0 " is changed to the trouble-free situation of sensing element) is for the signal bias amount of estimating the test combination then needs to carry out 3
2=9 set-up procedures are as follows:
First transducing signal of step 1-3 remains unchanged, and another transducing signal carries out three kinds and changes {+δ
2,-δ
2, 0};
First transducing signal+δ of step 4-6
1, another transducing signal carries out three kinds and changes {+δ
2,-δ
2, 0};
First transducing signal-δ of step 7-9
1, another transducing signal carries out three kinds and changes {+δ
2,-δ
2, 0}.
For a kind of parallel fault sensing element method for searching that is similar to tree-shaped path search, at 9 steps of each fault combination of estimation, each node need carry out 9 times and decompose below, therefore with this algorithm parallel 9 fork tree algorithms (as Fig. 3) of called after visually.The algorithm ground floor is parallel m node, and each node carries out the calculating of one time 9 fork tree by 9 set-up procedures, and defines a SSE matrix and be used for the fault location sensing element, referring to formula 3:
Wherein m is the number of fault combination, and the row, column value of each data is represented the ordinal number (1～m) and adjustment mode (1～9) of fault combination respectively.
With every group of test sample book fan-in network, by 9 parallel fork tree algorithms 9 signal bias operation is respectively carried out in m possible fault combination, result of calculation in the input matrix (formula 3), is obtained min (SSE successively
(i, j)), (i≤m, j≤9), then corresponding ranks positional value (i, j) fault location sensing element combination fast according to this value.
If have three road sensing elements to break down in 10 road signals simultaneously, then may exist
Plant the fault-signal situation, the step at least 3 of estimation side-play amount
3=27; If have a road sensing element to break down simultaneously in 10 road signals, the fault-signal combined number is
Kind, the step at least 3 of estimation side-play amount
aIndividual, corresponding calculated amount can be geometric series to be increased, and is very crucial work so improve algorithm raising localization of fault speed.
Be generalized to IEEE 1451 intelligence sensors that connect n the relevant property of signal sensing element, if wherein a (a＜n) there is fault in individual element, then all possible breakdown sensing element numbers
The step of estimation side-play amount is at least 3
aIndividual.
Aforesaid operations has been realized the location of multichannel fault sensing element, and the intelligence sensor that continues to have connected 10 road sensing elements (wherein having the two-way fault) is an example below, and the self-recovery method of fault sensing element signal is described in detail in detail.
According to the transducing signal internal correlation that neural network is grasped, orient the fault sensing element after, 9 fork tree algorithms further expand, and can be used for recovering the fault sensed values.A node choosing SSE minimum in the child node layer changes side-play amount step-length δ as the father node of one deck down, proceeds 9 fork trees and launches (as Fig. 4).Repeat this step, up to SSE less than the setting threshold Δ
SSETill, by reading each sensed values X of this moment
R, can realize the reconstruct of transducing signal, then self-healing transducing signal is:
X
R＝[x
1，x
2，...，x
j+δ
j，...，x
k+δ
k，...，x
10](4)
For the situation that has connected 10 road sensing elements (have simultaneously three road or more multichannel sensing element fault), its transducing signal also is to adopt the mode of similar expansion 9 fork trees from rejuvenation, father node by one deck under the node conduct choosing SSE minimum in the child node layer, change side-play amount step-length δ, proceed tree-shaped expansion.Repeat above-mentioned steps, up to SSE less than Δ
SSETill, by reading the sensed values X of reconstruct this moment
R, realize that transducing signal is from recovering.
Be generalized to one and connected the relevant sensing element of n road signal, if wherein a (a＜n) there are IEEE 1451 intelligence sensors of fault in circuit component, and behind the fault location sensing element, its signal also is by 3 from rejuvenation
aIndividual signal set-up procedure, the tree-shaped expansion of proceeding to expand.When SSE less than Δ
SSE, realize the recovery certainly of multichannel fault sensing element signal.
In the present embodiment, obtain the SSE matrix such as the following formula of certain group sensing data:
Min (SSE
(i, j) _ 1)=SSE
(16,4) _ 1=0.95231, its line number i equals 16, represents the 16th kind of permutation and combination, looks into sensing fault combination ordering and sees table 1, can locate the corresponding failure sensing element and be combined as { x
2, x
9; Columns j equals 4, represents the 4th kind of compensation way, i.e. { x
2+ δ
1, x
9+ δ
2, therefore, can continue to change step-length δ, utilize expansion 9 fork tree algorithms that it is reconstructed calculating.When expansion depth is 5, SSE
(16,4) _ 5=0.00003＜0.0001=Δ
SSE, can think that signal reconstruction is successful at this moment, overall response time is 1.154 seconds.
Table 1
Below with a transducing signal related close phthalic anhydride (Phthalic Anhydride, C
8H
4O
3) the reactor model is example, embodiment of the present invention is described further in detail:
The main production raw material of phthalic anhydride is o-xylene (Ortho-xylene), and its production technology is to use air to carry out fixed bed catalytic oxidation in reactor to produce continuously.Obtain stable product quality, must the strict response parameters such as charging rate, temperature and pressure of controlling reactor.Fig. 5 is based on the phthalic anhydride still sensing and the controlling schemes figure of neural network, in this scheme, relates to 3 tunnel temperature of reaction kettle amount, comprising: high-order temperature T
H, the meta temperature T
MAnd low level temperature T
LNo. 2 reactor amount of pressure comprise still roof pressure power P
HWith still bottom pressure P
L3 road mass flows comprise feed rate F
I, salt receiving velocity F
CWith chilled water speed F
L1 road cold salt temperature amount T
CAnd 1 road reacting gas temperature amount T
OEtc. quantity of information, these measurers have certain relevance, are fit to use multichannel sensing element localization of fault and the signal recovery method that the present invention proposes.
The above; only for the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, and anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (7)
1. many sensing elements of relevance transducing signal localization of fault and transducing signal self-recovery method is characterized in that, described method comprises:
A, obtain training sample;
B, foundation meet the neural network of multichannel sensing element signal association, and the relevance between the study transducing signal;
C, utilize each signal association sexual intercourse of study, will contain the transducing signal input neural network of fault sensing element,, input signal is carried out fault judgement according to the difference between network output signal and the input signal;
D, orient multichannel fault sensing element;
E, recovery multichannel fault transducing signal.
2. relevance transducing signal many sensing elements localization of fault according to claim 1 and transducing signal self-recovery method, it is characterized in that, described D specifically comprises: change the value of multichannel sensing element signal in fixed range, make it meet all each road transducing signal x
iBetween relevance relation, i=1 wherein, 2 ..., n, and orient multichannel fault sensing element according to the difference change conditions of network output signal and input signal.
3. relevance transducing signal many sensing elements localization of fault according to claim 1 and transducing signal self-recovery method, it is characterized in that, described E specifically comprises: concern according to the transducing signal relevance, continue to change the signal of fault location sensing element, make the output signal of neural network and input signal difference less than setting threshold, thereby recover the fault transducing signal automatically.
4. relevance transducing signal many sensing elements localization of fault according to claim 1 and transducing signal self-recovery method, it is characterized in that, the neural network algorithm of multichannel sensing element signal association is to utilize a kind of symmetrical topological structure neural network algorithm, and learns described relevance by training sample.
5. relevance transducing signal many sensing elements localization of fault according to claim 4 and transducing signal self-recovery method is characterized in that, the symmetrical topological structure neural network of described foundation has unity gain, its input vector x
iEqual output vector y
i, i=1 wherein, 2 ..., n.
6. according to claim 1,2 or 4 described relevance transducing signal many sensing elements localization of fault and transducing signal self-recovery methods, it is characterized in that, after setting up symmetrical topological structure neural network, by definition neural network output vector y
iWith input vector x
iBetween error sum of squares
Index is passed judgment on the factor as multichannel sensing element fault.
7. according to claim 1-6 each described relevance transducing signal many sensing elements localization of fault and transducing signal self-recovery method, it is characterized in that described method is: at each road transducing signal x
iBetween have relevance, and each road transducing signal all can impact down other road transducing signal and be suitable for, i=1 wherein, 2 ..., n is expressed as with mathematical model: x
1=f
1(x
2, x
3..., x
n), x
2=f
2(x
1, x
3..., x
n) ..., x
n=f
n(x
1, x
2..., x
N-1).
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| CN107636693A (en) * | 2015-03-20 | 2018-01-26 | 弗劳恩霍夫应用研究促进协会 | Assigned for the relevance scores of artificial neural network |
| CN108416309A (en) * | 2018-03-14 | 2018-08-17 | 揭阳职业技术学院 | A kind of intelligence sensor multiple faults transducing signal reconstructing method |
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| CN107636693A (en) * | 2015-03-20 | 2018-01-26 | 弗劳恩霍夫应用研究促进协会 | Assigned for the relevance scores of artificial neural network |
| CN107636693B (en) * | 2015-03-20 | 2022-01-11 | 弗劳恩霍夫应用研究促进协会 | Relevance score assignment for artificial neural networks |
| CN108416309A (en) * | 2018-03-14 | 2018-08-17 | 揭阳职业技术学院 | A kind of intelligence sensor multiple faults transducing signal reconstructing method |
| CN108416309B (en) * | 2018-03-14 | 2022-02-18 | 揭阳职业技术学院 | Multi-fault sensing signal reconstruction method for intelligent sensor |
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