CN100387901C - Method and apparatus for realizing integration of fault-diagnosis and fault-tolerance for boiler sensor based on Internet - Google Patents

Abstract

The present invention relates to a method and an apparatus for realizing the integration of fault diagnosis and fault tolerance for a boiler sensor based on the Internet, wherein the control method is applied to a three-layer neuronal network. An input mode pair and an output mode pair are determined through collecting voltage values corresponding to data measured by a sensor and grouping the data, the grouped data is used for training the neural network of a joint controller of a blurring cerebellum model, and then the fault diagnosis and the fault tolerance are carried out. In order to realize the method, the present invention also correspondingly provides a control apparatus. The present invention realizes the real-time monitoring for a complex process through researching the key technology for the integration of the fault diagnosis and the intelligent failure-tolerance control for the sensor under the Internet network environment. Occasions when the sensors are possibly applied are taken into account, and the redundancy of much sensor information in some actual processes is also taken into account, so the present invention can also be applied to industrial fields based on the Internet, such as underwater robots, chemical industries, etc.

Description

Boiler sensor fault diagnosis and fault-tolerant integral method and device based on the Internet net

Technical field

The invention belongs to network environment fault diagnosis and fault-toleranr technique field.A kind of boiler sensor fault diagnosis and fault-tolerant incorporate control method and device based on the Internet net is provided especially, a kind of control method that improves the boiler operatiopn reliability promptly is provided.

Background technology

Along with industrial process more and more trends towards networking and complicated, and the appearance of extensive high-caliber boiler complex automatic system, outstanding day by day to the requirement of steam generator system control quality.In to the control procedure of steam generator system, just may cause the massive losses of personnel and property in case have an accident, therefore, to on-line monitoring, diagnosis and the fault-tolerant importance of weighing the level of production and control level height that become of running status, product quality in the production process, also be the key technology that further improves product quality and production efficiency simultaneously.Conscientiously ensure the reliability and security of modern complex process, have crucial meaning, obtained paying much attention to widely both at home and abroad.

At present, steam generator system (as shown in Figure 1) sensor fault diagnosis and fault-tolerant control actual can use mainly contain following several frequently seen method:

(1) based on method of expert system

The general knowledge of depositing in the knowledge base is the working environment of system, and systematic knowledge has reflected the structure knowledge of the operation principle and the system of system.Rule set is one group of rule, the causality of reflection system.Man-machine interface can be some characteristic quantities that database increases before the system failure and fault observes when taking place.The diagnostic program of expert system is under the support of knowledge base and database, and the various rules of integrated use are carried out a series of reasoning, can also call various application programs at any time in case of necessity.It is in operation after the user asks for necessary information, just can directly find final fault or most possible fault apace, is come the accuracy of authentication system failure judgement again by the user.

(2) based on the diagnostic method of fault tree

This is a method for diagnosing faults commonly used in real system, and needed prerequisite is the priori of relevant fault and reason and the knowledge of fault rate.Diagnostic procedure is the fault tree that begins and be configured to step by step a handstand from the final fault of system.By the heuristic search meeting of this fault tree being found the final cause of fault.Reasonably the real-time dynamic data of using system will help the carrying out of diagnostic procedure in the enquirement process.The new development of present this method is to generate fault tree by computer is automatic or auxiliary, and generates the search engineering of fault tree automatically.

(3) based on the diagnostic method of pattern-recognition

The performing step of this method is:

1. the formation of fault secret room vector, also promptly select can expression system the vector set of malfunction.

2. the extraction of characteristic vector.Because the importance difference of each parameter in the fault mode vector, they are also not necessarily separate, therefore therefrom select the characteristic parameter the most responsive to malfunction, and the constitutive characteristic vector set has also promptly constituted the reference mode collection of fault.

3. the formation of discriminant function.It is made of in some way characteristic vector, is used to discern current state and belongs to which reference mode, also is which kind of malfunction is system belong to.

To the time delay under the Internet network environment and these novel faults of data-bag lost, above method is also inapplicable, does not also have good practical approach at present, therefore must seek new method, the invention provides a kind of new method.

Summary of the invention

The invention provides a kind of boiler sensor fault diagnosis and fault-tolerant incorporate method and device,, judged whether that fault takes place, and when fault takes place, carried out fault-tolerant by fault diagnosis and fault-tolerant integral method based on the Internet net.

Apparatus of the present invention comprise that remote fault diagnosis tolerant system, Internet net, host computer, slave computer, on-the-spot sensing become and send part and control section, wherein on-the-spot sensing becomes send part to comprise instrumentations such as temperature, liquid level, flow, pressure, and control section comprises frequency converter, IGCT, control valve etc.In the on-the-spot installation and measuring instrument of boiler, instrumentation is delivered to slave computer with the signal of gathering, regularly will gather signal is sent to host computer to slave computer, host computer passes to the remote fault diagnosis fault-tolerant control system to the data of accepting by the Internet net, carry out fault diagnosis and robust parsing, then control signal is sent it back host computer by the Internet net, host computer sends to slave computer to signal again, is transmitted control signal to control section by slave computer and finishes control.

The each several part function of apparatus of the present invention:

(1) on-the-spot sensing becomes and send part: comprise that instrumentations such as temperature, liquid level, flow, pressure are made up of sensor, be responsible for parameter acquisition and transmit;

(2) slave computer: be responsible for the signal A/D conversion of gathering, and signal is sent to host computer; Control signal D/A conversion, the control steam generator system;

(3) host computer: collect local slave computer data, and give remote fault diagnosis and fault-tolerant control system by the Internet network transmission, the control signal by Internet net receiving remote fault diagnosis and fault-tolerant control system send sends slave computer to;

(4) Internet net: remote fault diagnosis and fault-tolerant control system receive and send signal by the Internet net;

(5) control section: receive the fault-tolerant control signal that transmits by slave computer, control is realized at the scene;

The present invention adopts neutral net as the state estimator of handling redundant sensor output data.

Under many circumstances, when the input-output data are when interrelating with a unknowable system, when perhaps the model of system is too complicated, the model of system can not be set up, for the various large-sized power devices under the Internet network environment, these problems are just very outstanding, also just can't use based on the various analysis redundancy methods of model in this case.At this moment, the mapping performance of neural network structure just seems particularly useful.

The neural network structure that adopts among the present invention is BP (Back-Propagation) net as shown in Figure 2, it is a kind of neuroid with three layers, each neuron is realized full the connection between its left and right each layer, promptly each neuron of left side layer all has with each neuron of right layer and is connected, and does not have connection between each neuron of levels.The BP network is by there being the teacher learning mode to train, and after a pair of mode of learning offers network, its neuronic activation value will be propagated to output layer through each intermediate layer from input layer, and each neuron output of output layer is corresponding to the network response of input pattern.Then, according to reducing the principle of wishing output and actual output error, get back to input layer from output layer at last through each intermediate layer and successively revise each connection weight.Because this makeover process successively carries out from outputing to input, so claim that it is " an error Back-Propagation algorithm ".Along with constantly carrying out of this error Back-Propagation training, network also improves constantly the accuracy of input pattern response.

Because the BP network has the hidden layer that mediates, having corresponding learning rules can follow, and can train this network, makes it have recognition capability to nonlinear model.The learning algorithm that particularly its mathematical meaning is clear and definite, step is clearly demarcated more makes it be with a wide range of applications.

The learning process of BP net mainly is made up of four parts:

(1) input pattern saequential transmission broadcast-input pattern propagate to calculate to output layer through the intermediate layer by input layer,

(2) error of the contrary propagation-output of output error is passed through the intermediate layer to input layer by output layer,

(3) circulation memory training-pattern saequential transmission broadcast with the computational process of error Back-Propagation repeatedly alternate cycles carry out,

(4) whether learning outcome differentiation-judgement global error tends to minimum.

Introduce and analyze these four processes below respectively:

(1) input pattern is saequential transmission broadcast

This process mainly is to utilize input pattern to obtain its pairing actual output.

If input mode vector is $A_k=[a_1^k,a_2^k,\·\·\·,a_n^k]$

(k=1,2 ..., m; M-mode of learning logarithm; N-input layer unit number)

Be output as with the corresponding hope of input pattern $Y_k=[y_1^k,y_2^k,\·\·\·,y_q^k]$

(q-output layer unit number)

According to BP neuron models principle, calculate the activation value of intermediate layer neuron j:

$s_j=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{W}_{\mathrm{ij}}\·a_i-{\mathrm{\θ}}_j(j=\mathrm{1,2},\·\·\·,p)---\left(1\right)$

W in the formula _IjThe connection weight of-input layer i unit to j unit, intermediate layer;

θ _jThe threshold value of-intermediate layer neuron j;

P-middle layer elements number.

Activation primitive adopts the S type function, promptly

$f\left(x\right)=\frac{1}{1+\exp (-x)}---\left(2\right)$

Here why selecting the S type function to net neuronic activation primitive as BP is that continuously differentiable divides because of it, and more approaches the signal output form of biological neuron.

Bringing top activation value into can get j unit, intermediate layer in the activation primitive output valve is

$b_j=f\left(s_j\right)=\frac{1}{1+\exp (-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{W}_{\mathrm{ij}}\·a_i+{\mathrm{\θ}}_j)},(j=\mathrm{1,2},\·\·\·,p)---\left(3\right)$

Threshold value θ _jEqually with weights in learning process also constantly be corrected.The effect of threshold value is reflected on the curve of output of S function.

In like manner, can try to achieve the activation value and the output valve of output:

If the activation value of output layer t unit is l _t, then

$l_t=\underset{j=1}{\overset{p}{\mathrm{\Σ}}}{V}_{\mathrm{jt}}\·b_j-{\mathrm{\γ}}_t---\left(4\right)$

If the real output value of output layer t unit is c _t, then

$c_t=f\left(l_t\right)=\frac{1}{1+\exp (-\underset{j=1}{\overset{p}{\mathrm{\Σ}}}{V}_{\mathrm{jt}}\·b_j+{\mathrm{\γ}}_t)},(t=\mathrm{1,2}\·\·\·q)---\left(5\right)$

V in the formula _JtJ unit ,-intermediate layer to output layer t unit connection weight;

γ _t-output layer t cell threshode;

F-S type activation primitive.

Utilize the above various saequential transmission that just can calculate an input pattern to broadcast process.

(2) output error is contrary propagates

Pattern in the first step is saequential transmission broadcast the real output value that has obtained network in the calculating, when its error is greater than the numerical value that limited in other words when the output valve of these actual output valves and expectation is different, will proofread and correct network.

The correction here from after carry out forward, so be called error Back-Propagation, be during calculating from the output layer to the intermediate layer, again from the intermediate layer to the input layer.

The correction error of output layer is:

$d_t^k=(y_t^k-c_t^k)\·f^{\′}\left(l_t^k\right)=(y_t^k-c_t^k)\·c_t^k\left({1-c}_t^k\right),t=\mathrm{1,2},\·\·\·,q,k=\mathrm{1,2},\·\·\·---\left(6\right)$

Y in the formula _t ^k-desired output;

c _t ^k-actual output;

F '-to the output layer function derivative.

What adopt here is a kind of delta learning rules.

The correction error of j unit, intermediate layer is:

$e_j^k=(\underset{t=1}{\overset{q}{\mathrm{\Σ}}}{V}_{\mathrm{jt}}\·d_t^k)\·f^{\′}\left(s_j^k\right)=(\underset{t=1}{\overset{q}{\mathrm{\Σ}}}{V}_{\mathrm{jt}}\·d_t^k)\·b_j\·(1-b_j),(j=\mathrm{1,2},\·\·\·p,k=\mathrm{1,2},\·\·\·,m)---\left(7\right)$

Here, the correction error of each middle layer elements is all transmitted and is produced by q output layer unit correction error.After correction error is tried to achieve, then can utilize d _t ^kAnd e _j ^kAlong contrary direction successively adjust output layer to the intermediate layer, the intermediate layer is to the connection weight of input layer.

Be respectively for output layer to the correcting value of intermediate layer connection weight and the correcting value of output layer threshold value:

$V_{\mathrm{jt}}(N+1)=V_{\mathrm{jt}}\left(N\right)+\mathrm{\α}\·d_t^k\·b_j\⇒\mathrm{\Δ}{V}_{\mathrm{jt}}=\mathrm{\α}\·d_t^k\·b_j^k---\left(8\right)$

${\mathrm{\γ}}_t(N+1)={\mathrm{\γ}}_t\left(N\right)+\mathrm{\α}\·d_t^k\⇒\mathrm{\Δ}{\mathrm{\γ}}_t=\mathrm{\α}\·d_t^k---\left(9\right)$

B in the formula _j ^kThe output of j unit ,-intermediate layer;

d _t ^kThe correction error of-output layer.

J=1,2 ..., p t=1,2 ..., q k=1,2 ..., intermediate layer, m 0＜α＜1 (learning coefficient) to the correcting value of input layer is:

$W_{\mathrm{ij}}(N+1)=W_{\mathrm{ij}}\left(N\right)+\mathrm{\Δ}{W}_{\mathrm{ij}}=W_{\mathrm{ij}}\left(N\right)+\mathrm{\β}\·e_j^k\·a_i^k\⇒\mathrm{\Δ}{W}_{\mathrm{ij}}=\mathrm{\β}\·e_j^k\·a_i^k---\left(10\right)$

${\mathrm{\θ}}_j(N+1)={\mathrm{\θ}}_j\left(N\right)+\mathrm{\Δ}{\mathrm{\θ}}_j={\mathrm{\θ}}_j\left(N\right)+\mathrm{\β}\·e_j^k\⇒\mathrm{\Δ}{\mathrm{\θ}}_j=\mathrm{\β}\·e_j^k---\left(11\right)$

E in the formula _j ^kThe correction error of j unit ,-intermediate layer.

I=1,2 ..., n 0＜β＜1 (learning coefficient).

Here as can be seen:

A) correcting value is directly proportional with error, and promptly error is big more, and the amplitude of adjustment is just big more, and this physical significance is conspicuous.

B) correcting value is directly proportional with the size of input value, here because input value is big more, just seems active more in current learning process, so the adjusting range of coupled weights just should be big more.

C) correcting value is directly proportional with learning coefficient.Usually learning coefficient is accelerated for making whole learning process between 0.1-0.8, does not cause concussion again, can adopt the method for learning rate changing, promptly gets bigger learning coefficient at the study initial stage, along with the carrying out of learning process reduces its value gradually.

(3) circulation memory training

For the output error that makes network is tending towards minimum.For each group training mode of BP net input, generally to just can make network remember this pattern through hundreds of inferior even up to ten thousand time circulation memory training.

In fact this circulation memory training is exactly that the input pattern that repeats to introduce is above repeatedly saequential transmission broadcast and the contrary communication process of output error.

(4) differentiation of learning outcome

After each circulation memory training finishes, all to carry out the differentiation of learning outcome.The purpose of differentiating mainly is to check whether output error is little of the degree that allows.If the little degree that allows that arrived just can finish whole learning process, otherwise also will carry out circuit training.The study process of training in other words is that the network global error tends to minimizing process.

According to top analysis, can obtain the concrete steps of the whole learning process of BP network:

(1) each connection weight W is given in initialization _Ij, V _JtAnd threshold value θ _j, γ _t, and give random value between [1 ,+1].

(2) picked at random one pattern is right $A_k=[a_1^k,a_2^k,\·\·\·{,a}_n^k],$ $Y_k=[y_1^k,y_2^k,\·\·\·,y_q^k],$ Offer network.Use input pattern $A_k=[a_1^k,a_2^k,\·\·\·,a_n^k],$ Connection weight W _IjWith threshold value θ _jCalculate each neuronic input s of intermediate layer _j(activation value) uses s then _jObtain the output b in intermediate layer by activation primitive _j, i.e. formula (3);

(3) the output b in usefulness intermediate layer _j, connection weight V _JtAnd threshold gamma _tCalculate the input l of each unit of output layer _t(activation value) uses l then _tCalculate the output c of each unit of output layer by activation primitive _t, i.e. formula (5);

(4) with wishing output mode $Y_k=[y_1^k,y_2^k,\·\·\·,y_q^k],$ The actual output of network c _tCalculate the correction error d of each unit of output layer _t ^k, i.e. formula (6);

(5) use V _Jt, d _t, b _jCalculate the correction error e in intermediate layer _j ^k, i.e. formula (7);

Use d _t ^k, b _j, V _JtAnd γ _tCalculate next time intermediate layer and the new connection weight between the output layer, i.e. formula (8) and (9);

(6) by e _j ^k, a _i ^k, W _IjAnd θ _jCalculate next time input layer and the new connection weight between the intermediate layer, i.e. formula (10) and (11);

(7) choose next mode of learning to offering network, turned back to for the 3rd step, until whole m pattern to having trained.

(8) right from pattern of m mode of learning centering picked at random again, turned back to for the 3rd step, until network global error function E less than predefined limits value (network convergence).

In above step, (3)-(6) are " the contrary communication process " of network error for " saequential transmission is broadcast process " of input pattern, (7), and (8) then finish training and convergence process.

Boiler sensor fault diagnosis and the concrete control procedure of fault-tolerant incorporate control method based on the Internet net are carried out according to following steps:

Step 1, image data: the data corresponding voltage value x (1) that pick-up transducers is measured, x (2) ..., x (n);

Step 2, packet, determine that input and output mode is right:

(1) determine the m value, m is the quantity of each input and output mode centering input data, i.e. the quantity of observation, and m value is more definite according to the experimental data and the measurement result of test of many times acquisition;

(2) for single-sensor, n the data of being gathered are divided into the k group, k=n-(m-1), every group has m+1 value, wherein preceding m value is as the input value of neutral net input node, m+1 value forms sample as the neutral net output after training, promptly since the 1st data, with preceding m the data x (1) of sensor output, x (2),, x (m) is as the input of neutral net, forms the 1st group of sample with m+1 the data x (m+1) of sensor output as the output of neutral net; Since the 2nd data, with m data x (2), x (3) ..., x (m+1) is as the input of neutral net, forms the 2nd group of sample with x (m+2) as the output of neutral net; The rest may be inferred, the sensor output sequence can be divided into the k group, every group of m+1 data, and it is right to form k input and output mode; The data of grouping are used for the single neutral net of off-line training, as shown in Figure 3, this moment p=m, q=1, training obtains each weights;

For multisensor, the quantity of setting sensor is N, and the number of neutral net also is N so, be N neutral net parallel connection, each sensor is gathered n data, equally n the data of being gathered is divided into k group, k=n-(m-1), every group has m+1 value, wherein preceding m input value that is worth as each neutral net input node, m+1 output that is worth as each neutral net, like this, the input data are total up to N * m, and the output data are total up to N;

Step 3, with the grouping data be used for the off-line training neutral net, obtain each weights by the BP network learning procedure;

Step 4, fault diagnosis (suppose arbitrary moment have only a sensor to break down at most): for the single-sensor situation, it is any K moment, the neutral net input value is the data of preceding K-m pick-up transducers measurement constantly, be x (K-m), x (K-m+1),, x (K-1), the neutral net output valve is that predicted value is

Work as predicted value With the difference of the measured value of sensor during greater than setting threshold, or when failing to obtain the measured value of sensor owing to time delay, the data-bag lost of network, then failure judgement takes place; For the multisensor situation, suppose that arbitrary moment has only a sensor to break down at most, number of sensors is N, neutral net input data are:

[x ₁(K-m), x ₁(K-m+1) ..., x ₁(K-1)] be first grouped data,

[x ₂(K-m), x ₂(K-m+1) ..., x ₂(K-1)] be second grouped data,

……、

[x _N(K-m), x _N(K-m+1) ..., x _N(K-1)] be N grouped data,

Neutral net output valve i.e. K predicted value constantly is I=1,2 ..., N.The difference of the output of i neutral net and the measured value of i sensor is during greater than setting threshold, or owing to the time delay of network, when data-bag lost fails to obtain the measured value of i sensor, then failure judgement generation;

Step 5, fault-tolerant:, use for the single-sensor situation Replace the output of sensor; For the multisensor situation, i neutral net output

Replace the output of i sensor.

In above-mentioned fault diagnosis and fault-tolerant integrated control, for multisensor, suppose that arbitrary moment has only a sensor to break down at most, because the sampling interval is very short, so this supposition can tally with the actual situation.

Control procedure of the present invention has following advantage:

1, the present invention realizes complex process is monitored by the research to sensor fault diagnosis under the Internet network environment and integrated this key technology of intelligent fault-tolerance control.

2, control method of the present invention had both considered that sensor can applicable occasion, considered the redundancy of numerous sensor informations in some real processes again, thereby achievement in research of the present invention can also be applied to industrial circles such as underwater robot based on Internet net, chemical industry.

Description of drawings

Fig. 1 is the structural representation of apparatus of the present invention;

Fig. 2 is the neural network structure schematic diagram;

Fig. 3 is sensor fault diagnosis and fault-tolerant integrated control principle figure;

Fig. 4 is apparatus of the present invention structural representation;

Fig. 5 is the control flow chart of the inventive method;

Prognostic chart when Fig. 6 breaks down for the boiler liquid level sensor;

Prognostic chart when Fig. 7 breaks down for the boiler water temperature sensor;

Prognostic chart (multisensor) when Fig. 8 breaks down for the boiler liquid level sensor;

Wherein, VD1, VD2-solenoid valve, T1-VT-shifts to regulation and control, LT-3-boiler liquid level sensor, LT-4-elevated tank liquid level sensor, TIT-1-boiler water temperature sensor, TIT-2-boiler jacket water (J.W.) temperature sensor.

The specific embodiment

Be described in detail below in conjunction with the specific embodiment of accompanying drawing apparatus of the present invention.

As shown in Figure 4, apparatus of the present invention comprise that remote fault diagnosis tolerant system, Internet net, host computer, slave computer, on-the-spot sensing become and send part and control section that the boiler Process Control System mainly is made of two parts:

(1) variable frequency pump, the water drainage-supply system that high-order constant voltage water tower and cistern constitute.In this system, comprise one on the original-pack body of stainless steel water pump of German Grandfos company, one of the original-pack many control modes vector type of Siemens AC converter.

(2) be distributed in the controlled process that five unit on three different aspects are formed, these five unit are respectively:

A) the hot-water boiler unit of cooling water jecket is arranged.The hot-water boiler unit is the core of whole controlled process, has liquid level and detect and the transmission device table on hot-water boiler, and chuck coolant water temperature and boiler water temperature temperature detection sensor and boiler water add thermal actuator.

B) 2 groups of flow detection are carried out assembled unit with adjusting.Each group all has an electromagnetic type flow meter and a German Bao De pneumatic control valve of company's piston type and two formula magnetic valve to constitute, and it also is that the another important process of whole controlled process detects and adjusting executing agency.

C) pressure sensing cell in a loop.

D) double volume in parallel unit.2 back pressure type liquid levels detections are housed respectively again on this unit send device with change.

Five unit in these two parts and the controlled process, all independent relatively each other, the user can freely choose as the case may be, has bigger flexibility.

Boiler water temperature signal in the boiler Process Control System, boiler chuck water temperature signal detect by platinum resistance thermometer.Platinum resistance thermometer utilizes the temperature variant characteristic of electric parameter to come detected temperatures.In native system, survey the platinum resistance thermometer of boiler water temperature, its τ _0.5=15 seconds, the platinum resistance thermometer of survey chuck water temperature, its τ _0.5=5 seconds.(according to manufacturer's definition, τ _0.5After being meant the dut temperature step disturbance, platinum resistance thermometer measures this step temperature 50% this required time.)

What the measurement of liquid level was adopted is that the production of Hefei god Electrical Appliances Co., Ltd is output as standard signal 4-20mA, and range is that the HM model pressure type sensor of 6Kpa is measured.

Flow sensor adopts is the electromagnetic flowmeter of the LDZ model of being produced by Guanghua Instrument and Meter Plant.The signal of electromagnetic flow transducer output is voltage, the current signal of standard, and people can't read the size of flow intuitively, and this system has also adopted electromagnetic flux converter for this reason.This converter can with the supporting use of the electromagnetic flow transducer of all size, the energy low level millivolt signal of autobiography sensor in the future converts to and proportional 0-10mA DC of flow or the output of 4-20mA DC standard signal, for the usefulness that shows, writes down, regulates control and flow rate calculation etc.What native system adopted is the electromagnetic flux converter of the LDZ-4B model of Guanghua Instrument and Meter Plant production.

The control procedure of this boiler Process Control System:

(1) this boiler Process Control System can be controlled flow of inlet water and water flow, by the water inlet control valve with go out water regulating valve and realize.The water inlet control valve is a QSVP series intelligent electric single-seat adjusting valve with going out water regulating valve, it very wins the intelligent electric actuator by QSL and combines with the homemade valve of high-quality and form, be a kind of high performance control valve, be applicable to the fluid of various different pressures and temperature and leaking demanding occasion.Electric operator is accepted control signals such as 0-10mA/4-20mA/0-5V/1-5V, changes the aperture of valve, and the isolation signals with valve opening feeds back to boiler control system simultaneously, realizes pressure, temperature, flow, the isoparametric adjusting of liquid level.The adjusting of this boiler Process Control System liquid level is by the water inlet control valve and go out water regulating valve and realize.

(2) temperature being controlled, is to realize with the voltage that the angle of flow of IGCT VT changes electric heater by changing electrical heating.

(3) Frequency Converter Control.Have two kinds of methods to drive the pump motor operation in native system, a kind of is three power supplys of civil power 50Hz, and when selecting the method, water pump moves under its rated speed; Another kind method is that AC frequency conversion drives, and when selecting the method, frequency converter receives the given signal of standard of 4-20mA, and three variable-frequency power sources of output 0-50Hz remove to control the rotating speed of pump motor.But no matter choosing any method moves water pump, its motor can not turn round immediately, must wait until that " enabling " switch becomes after " ON ", and pump motor can move.

(4) also has a kind of device of controlling flow, two formula magnetic valves.The Push And Release of magnetic valve can change water inlet and water flow.

Host computer and fault diagnosis and tolerant system are selected PENTIUM 4DELL computer for use, adopt WINDOW XP operating system.

Fault diagnosis and tolerant system operate on the PENTIUM 4DELL computer, adopt the VB7.0 programming software.

It is to adopt the VC++7.0 programming software that the signal of host computer and fault diagnosis and tolerant system transmits software.

In the on-the-spot installation and measuring instrument of boiler, instrumentation is delivered to slave computer with the signal of gathering, regularly will gather signal is sent to host computer to slave computer, host computer passes to the remote fault diagnosis fault-tolerant control system to the data of accepting by the Internet net, carry out fault diagnosis and robust parsing, then control signal is sent it back host computer by the Internet net, host computer sends to slave computer to signal again, finishes control.

The signal that detects through each sensor all is an analog quantity, and the driving signal that each executing agency needs also is an analog quantity, therefore, needs the conversion of analog/digital conversion and digital-to-analog.What slave computer adopted here is that A/D, the D/A integrated circuit board that magnificent company produces ground in Taiwan.Analog quantity is finished by the integrated circuit board of PCL-812PG model to the conversion of digital quantity, and this integrated circuit board has 16 A/D ALT-CH alternate channels, 2 D/A ALT-CH alternate channels, the input of 16 bit digital quantity, the output of 16 bit digital quantity.Apparatus of the present invention are used 8 A/D ALT-CH alternate channels, and employing is the voltage input, and scope is ± 5V.Digital quantity is finished by the integrated circuit board of PCL-726 model to the conversion of analog quantity, and this integrated circuit board has 6 D/A ALT-CH alternate channels, 16 bit digital inputs, the output of 16 bit digital, apparatus of the present invention are used 4 D/A ALT-CH alternate channels, employing be electric current output, scope is 4-20mA.

Fig. 5 is the control flow chart of the inventive method, specifically describes the inventive method to boiler sensor Fault Diagnosis and fault-tolerant incorporate control procedure below in conjunction with accompanying drawing.

Example 1, at first boiler being broken from the Internet network, is research object with the sensor of measuring boiler liquid level, water is extracted into elevated tank from tank after, by elevated tank to boiler water supply.Middle via magnetic valve and motor-driven valve, control the flow of water by the aperture of controlling motor-driven valve.Boiler liquid level is from initial value zero, and setting boiler liquid level is 300 millimeters.Adopt simple control, promptly when liquid level reaches 290 millimeters, close motor-driven valve.Sample in control procedure, sampling time interval is 3 seconds.Sampled signal is and the boiler liquid level corresponding voltage value, do not convert sampled value to liquid level in sampling process, but directly with the input signal of magnitude of voltage as neutral net.In diagnosis and fault-tolerant process, each signal all is that the form with voltage signal collects in the computer, is 1-5V.In the process of gathering signal, there is fluctuation in signal, at first adopts the mean filter method to carry out filtering here, promptly after each sampled point continuous sampling 10 times,, get its mean value again as this sampled value 10 sampled value additions, by the mean filter method, avoided the fluctuation of signal basically.

The deposit data of gathering is in database.Image data is shown below, and data are and the boiler liquid level corresponding voltage value in the formula, and unit is a volt.

V＝[1.0388 1.0401 1.0416 1.0498 1.0602 1.0602 1.0788 1.0788 1.1172

1.1215 1.1401 1.1542 1.1676 1.1902 1.1975 1.2192 1.2347 1.2482

1.2592 1.2802 1.3062 1.3156 1.3284 1.3528 1.3730 1.3831 1.3895

1.4017 1.4291 1.4447 1.4664 1.4819 1.5024 1.5070 1.5256 1.5326

1.5540 1.5793 1.5897 1.6077 1.6324 1.6516 1.6568 1.6760 1.6919

1.7020 1.7270 1.7380 1.7441 1.7728 1.7908 1.8066 1.8170 1.8329

1.8506 1.8591 1.8741 1.8890 1.9122 1.9226 1.9226 1.9522 1.9632

1.9882 1.9897 2.0081 2.0300 2.0517 2.0642 2.0691 2.0923 2.0932

2.1173 2.1353 2.1411 2.1631 2.1793 2.1899 2.2134 2.2229 2.2461

2.2546 2.2696 2.2803 2.2986 2.3203 2.3285 2.3444 2.3587 2.3743

2.3843 2.4005 2.4152 2.4283 2.4481 2.4677 2.4768 2.4924 2.5070

2.5189 2.5345 2.5470 2.5650 2.5851 2.5943 2.6062 2.6279 2.6450

2.6456 2.6666 2.6773 2.6953 2.7145 2.7261 2.7417 2.7560 2.7728

2.7789 2.8036 2.8162 2.8296 2.8384 2.8598 2.8830 2.8906 2.8989

2.9187 2.9248 2.9443 2.9562 2.9745 2.9843 2.9877 2.9944 2.9950

2.9950 2.9973 2.9976 2.9975 2.9976 2.9977 2.9974 2.9976 2.9986

2.9988 2.9990 2.9990 2.9988 2.9990 2.9989 2.9990 3.0017 3.0035]

The liquid level sensor output sequence x (1) that has obtained, x (2) ..., 153 data that x (153) is as implied above, when off-line training, after m is defined as 5, since the 1st data, with preceding 5 data x (1) of sensor output, x (2),, x (5) is as the input of neutral net, forms the 1st group of sample with the 6th the data x (6) of sensor output as the output of neutral net; Since the 2nd data, with 5 data x (2), x (3) ..., x (6) is as the input of neutral net, forms the 2nd group of sample with x (7) as the output of neutral net; The rest may be inferred, the sensor output sequence can be divided into 149 groups, every group of 6 data, it is right to form 149 input and output modes, then with these 149 input and output modes to network is carried out off-line training.During work, export 5 data x (149) of the 149th group with sensor, x (150) ..., x (153) is as the input of neutral net, and network output is the prediction data of sensor output

Boiler is inserted the Internet net, with the prediction data of sensor output With the difference and the threshold of the actual output of sensor, judge whether this sensor breaks down, when the prediction data of sensor output

With greater than the fault-free measured value 5% o'clock of the difference of the actual output of sensor, then liquid level sensor breaks down.As break down, then use predicted value Replace sensor output value.

When sensor breaks down, with the design sketch of neural network prediction value replacement actual detected value, as shown in Figure 6.When boiler liquid level reached 150 millimeters, the sensor of measuring boiler liquid level broke down, and output valve is 150 millimeters always.

If detecting the sensor of liquid level breaks down when boiler liquid level reaches 150 millimeters, as shown in Figure 6, at this moment, just fault has taken place in sensor, if the control to boiler liquid level in the system is still carried out according to the value of feedback of the sensor that breaks down, then adverse consequences can appear, so must find the replacement value of sensing data., replace detecting the output of boiler liquid level sensor here, thereby continue system is controlled with the neural network prediction value.Be implemented as follows: during the program operation, promptly the boiler liquid level sensor is monitored, so when boiler liquid level reaches 150 millimeters, comparison by predicted value and measured value, detecting the boiler liquid level sensor breaks down, corresponding voltage value is 2.9880 volts, can replace the output of boiler liquid level sensor like this by the neural network prediction value, make it as value of feedback, system can normally move, equally, this that comprises in the neutral net input value of boiler liquid level sensor is constantly also replaced by this predicted value, thereby, the output of boiler liquid level sensor constantly after can predicting as far as possible accurately, as can be seen from the figure, the difference of predicted value and ideal value is no more than 3 millimeters.

Example 2, at first boiler is broken from the Internet network, sensor with the measurement boiler water temperature is a research object, whether before the water in the boiler is heated, will check boiler liquid level more than 140 millimeters, and for personal safety, the heating water temperature had better not surpass 80 degree.Control procedure is to control temperature by the voltage that control is added in the IGCT two ends.Boiler water temperature is since 30 degree heating, and the setting boiler water temperature is elevated to 60 degree to be ended.

Sample in control procedure, sampling time interval is 5 seconds.Sampled signal is and the boiler water temperature corresponding voltage value, do not convert sampled value to water temperature in the sampling process, but directly with the input signal of magnitude of voltage as neutral net.

Also there is fluctuation in temperature signal, carries out filtering with Mean Method, has obtained effect preferably.

The boiler water temperature sensor output sequence that obtains is as follows, and data are and the boiler temperature corresponding voltage value in the formula, and unit is a volt.

V＝[2.2058 2.2079 2.2113 2.2198 2.2205 2.2348

2.2305 2.2446 2.2412 2.2482 2.2540 2.2650

2.2626 2.2559 2.2501 2.2610 2.2458 2.2552

2.2504 2.2012 2.2089 2.2025 2.2128 2.2189

2.2223 2.2458 2.2601 2.2891 2.3001 2.3212

2.3486 2.3721 2.3898 2.3999 2.4130 2.4124

2.4396 2.4600 2.4710 2.4884 2.5098 2.5162

2.5522 2.5595 2.5745 2.5830 2.5980 2.6068

2.6257 2.6346 2.6581 2.6666 2.6971 2.7200

2.7234 2.7402 2.7377 2.7707 2.7811 2.7951

2.8125 2.8204 2.8195 2.8473 2.8650 2.8598

2.8946 2.9010 2.9245 2.9126 2.9269 2.9492

2.9630 2.9819 2.9822 2.9880 3.0115 3.0304

3.0325 3.0386 3.0484 3.0692 3.0722 3.0902

3.0890 3.1082 3.1259 3.1223 3.1393 3.1573

3.1412 3.1726 3.1909 3.2043 3.2077 3.2010

3.2239 3.2275 3.2480 3.2391 3.2718 3.2806

3.2809 3.2907 3.3182 3.3112 3.3194 3.3313

3.3527 3.3618 3.3633 3.3759 3.3847 3.3878

3.3847 3.3923 3.3997 3.4048 3.4174 3.4280

3.4286 3.4283 3.4262 3.4421 3.4329 3.4344

3.4302 3.4341 3.4360 3.4369 3.4387 3.4280

3.4338 3.4366 3.4174 3.4369 3.4341 3.4293

3.4305 3.4238 3.4259 3.4302 3.4204 3.4271

3.4164 3.4015 3.4061 3.4152 3.3960 3.4079

3.4103 3.4055]

Obtain temperature sensor output sequence 152 groups of data as implied above, get m=5, so can be divided into 148 groups, with example 1, preceding 5 input data in every group of 6 data as neutral net, the 6th data are as the output data of neutral net, with these 148 groups of input and output modes to neutral net is trained.During work, with the input as neutral net of preceding 5 data of sensor output, network output is next prediction data constantly of sensor.

When sensor did not break down, effect that the neutral net of training is used for real system when predicting was very desirable, and at each sampled point, the neural network prediction value overlaps substantially with ideal value, meets the requirements of effect.

Boiler is inserted the Internet net, when the boiler water temperature sensor breaks down, can be with replace the breaking down output of sensor of neural network prediction value.As shown in Figure 7, reach 50 when spending at boiler water temperature, the sensor of measuring boiler water temperature breaks down, and output valve is 50 degree always, and corresponding voltage value is 2.9880 volts.

After the boiler water temperature sensor breaks down, if still control according to the current detection value, then inconceivable consequence can appear.In program, sensor measured value and neural network prediction value are compared, if greater than setting value, think that then sensor breaks down.After this measured value of sensor is replaced by the neural network prediction value, and program is controlled system according to this predicted value.Fault has taken place in the sensor of measuring now boiler water temperature when 50 spend because the neural network prediction value itself exists error, so moment error afterwards increase gradually, when reaching stable state, with the ideal value error greatly about about 1 degree.

Example 3, at first boiler being broken from the Internet network, is research object with 3 sensors, comprises the sensor of measuring No. 1 tank fill level, measures the sensor of No. 2 tank fill levels, measures the sensor of boiler liquid level.In the actual measurement process, the number of sensors of noting choosing is not for existing redundant number, the promptly selected same parameter of not duplicate measurements of sensor.The data corresponding voltage value that selected sensor is gathered is as follows, and its unit is a volt:

(1) boiler liquid level

V＝[1.0309 1.0318 1.0318 1.0413 1.0431 1.0431

1.0596 1.0837 1.1020 1.1020 1.1264 1.1371

1.1566 1.1679 1.1865 1.1917 1.1987 1.2231

1.2396 1.2457 1.2653 1.2909 1.3010 1.3080

1.3297 1.3403 1.3562 1.3702 1.3861 1.3977

1.4035 1.4276 1.4340 1.4572 1.4685 1.4874

1.4978 1.5131 1.5280 1.5439 1.5524 1.5729

1.5900 1.6006 1.6119 1.6254 1.6473 1.6586

1.6702 1.6837 1.6959 1.7105 1.7261 1.7297

1.7462 1.7603 1.7816 1.7917 1.8063 1.8149

1.8167 1.8503 1.8579 1.8661 1.8765 1.8945

1.9077 1.9229 1.9351 1.9510 1.9611 1.9711

1.9794 1.9949 2.0062 2.0169 2.0258 2.0435

2.0584 2.0700 2.0764 2.0972 2.1030 2.1222

2.1225 2.1451 2.1606 2.1643 2.1793 2.1902

2.1924 2.2141 2.2260 2.2406 2.2507 2.2696

2.2699 2.2876 2.3019 2.3065 2.3196 2.3380

2.3529 2.3621 2.3721 2.3883 2.3929 2.4106

2.4106 2.4280 2.4454 2.4606 2.4640 2.4673

2.4841 2.5003 2.5064 2.5165 2.5342 2.5470

2.5574 2.5699 2.5787 2.5861 2.6025 2.6138

2.6199 2.6370 2.6462 2.6602 2.6669 2.6791

2.6971 2.6971 2.7106 2.7200 2.7313 2.7435

2.7533 2.7762 2.7762 2.7856 2.7991 2.8085

2.8168 2.8366 2.8366 2.8510 2.8632 2.8787

2.8897 2.8976 2.9007 2.9126 2.9236 2.9309

2.9492 2.9550 2.9657 2.9712 2.9837 2.9929

2.9929 2.9929 2.9929]

(2) 1# tank fill level

V＝[3.9911 3.9911 3.9911 3.9908 3.9847 3.9795

3.9764 3.9688 3.9072 3.9072 3.9072 3.9072

3.9072 3.9072 3.9072 3.8922 3.8815 3.8721

3.8580 3.8553 3.8443 3.8266 3.8211 3.8004

3.8004 3.7909 3.7741 3.7680 3.7570 3.7436

3.7317 3.7277 3.7146 3.7061 3.6911 3.6823

3.6761 3.6557 3.6502 3.6386 3.6331 3.6215

3.6102 3.5968 3.5864 3.5745 3.5532 3.5522

3.5437 3.5352 3.5269 3.5193 3.5019 3.4927

3.4851 3.4738 3.4634 3.4518 3.4439 3.4366

3.4250 3.4097 3.4048 3.3942 3.3722 3.3722

3.3606 3.3508 3.3377 3.3365 3.3170 3.3035

3.2959 3.2913 3.2816 3.2733 3.2559 3.2492

3.2367 3.2327 3.2254 3.2172 3.2040 3.1995

3.1830 3.1778 3.1674 3.1534 3.1412 3.1381

3.1204 3.1204 3.1085 3.0978 3.0954 3.0731

3.0661 3.0600 3.0560 3.0426 3.0286 3.0286

3.0161 3.0032 2.9938 2.9858 2.9764 2.9742

2.9648 2.9498 2.9407 2.9288 2.9193 2.9160

2.9053 2.9010 2.8873 2.8732 2.8604 2.8571

2.8528 2.8403 2.8290 2.8265 2.8156 2.8088

2.8046 2.7872 2.7838 2.7652 2.7652 2.7512

2.7493 2.7414 2.7332 2.7103 2.7100 2.7008

2.6810 2.6810 2.6764 2.6688 2.6614 2.6511

2.6450 2.6361 2.6157 2.6141 2.6050 2.6047

2.5970 2.5876 2.5751 2.5735 2.5586 2.5571

2.5424 2.5391 2.5369 2.5204 2.5092 2.4985

2.4869 2.4869 2.4869]

(3) 2# tank fill level

V＝[3.9410 3.9410 3.9407 3.9240 3.9096 3.8953

3.8751 3.8699 3.8315 3.8315 3.8287 3.8235

3.8077 3.7894 3.7799 3.7708 3.7631 3.7537

3.7393 3.7299 3.7219 3.7094 3.6960 3.6859

3.6731 3.6649 3.6533 3.6407 3.6340 3.6255

3.6124 3.5995 3.5995 3.5831 3.5779 3.5648

3.5538 3.5425 3.5352 3.5208 3.5172 3.5019

3.4857 3.4830 3.4778 3.4592 3.4384 3.4384

3.4299 3.4222 3.4085 3.4061 3.3978 3.3911

3.3817 3.3670 3.3548 3.3505 3.3344 3.3295

3.3139 3.3047 3.2947 3.2861 3.2800 3.2639

3.2639 3.2574 3.2443 3.2352 3.2166 3.2126

3.2050 3.1995 3.1879 3.1689 3.1662 3.1604

3.1516 3.1369 3.1369 3.t274 3.1146 3.1064

3.0920 3.0783 3.0728 3.0637 3.0576 3.0502

3.0389 3.0298 3.0267 3.0087 3.0029 2.9932

2.9758 2.9758 2.9721 2.9556 2.9529 2.9459

2.9385 2.9224 2.9208 2.9007 2.8976 2.8882

2.8772 2.8711 2.8671 2.8528 2.8424 2.8424

2.8271 2.8143 2.8143 2.8085 2.7994 2.7902

2.7780 2.7713 2.7612 2.7570 2.7457 2.7365

2.7231 2.7225 2.7121 2.7026 2.6959 2.6855

2.6804 2.6764 2.6630 2.6566 2.6462 2.6373

2.6273 2.6242 2.6132 2.6031 2.6031 2.5955

2.5879 2.5803 2.5653 2.5613 2.5528 2.5449

2.5275 2.5275 2.5186 2.5177 2.5012 2.4988

2.4750 2.4731 2.4731 2.4707 2.4689 2.4689

2.4689 2.4689 2.4689]

Need at first to determine that the input and output mode of neutral net is right, be used for neural network training.Be example to judge whether No. 1 tank fill level sensor breaks down, its corresponding No. 1 neutral net then, No. 1 neutral net K input constantly is the preceding m value constantly that collects from No. 2 tank fill level sensors and boiler liquid level sensor, and No. 1 neutral net K output constantly is the predicted value of No. 1 tank fill level sensor.By experiment, the experimental result when getting different value according to m, this paper gets m=2.First preceding 2 moment data x with No. 2 tank fill level sensor outputs _{No. 2}(1), x _{No. 2}(2), boiler liquid level sensor preceding two 2 data x constantly _Boiler(1), x _Boiler(2) as the input of neutral net, with No. 1 next value x constantly of tank fill level sensor _{No. 1}(3) output as neutral net forms first group of sample; With data x _{No. 2}(2), x _{No. 2}(3), x _Boiler(2), x _Boiler(3) as the input of neutral net, with x _{No. 1}(4) output as neutral net forms second group of sample, and the rest may be inferred.Because have 165 groups of data, so it is right to form 163 input and output modes altogether.Here the input parameter that is noted that No. 1 neutral net can not comprise the input of No. 1 tank fill level sensor.In like manner, can set up No. 2 and No. 3 neutral nets of No. 2 tank fill level sensors and boiler liquid level sensor correspondence.So the present invention adopts neutral net, the Application of Neural Network of training in this boiler procedures system, is monitored boiler liquid level sensor, No. 1 tank fill level sensor, No. 2 tank fill level sensors.

Boiler liquid level variation from 0 millimeter to 300 millimeters, when the boiler liquid level sensor does not break down, effect that the neutral net of training is used for real system when predicting is very desirable, can see from data presented, at each sampled point, the error of neural network prediction value and ideal value meets the requirements of effect within 1 millimeter.

When sensor breaks down, with the design sketch of neural network prediction value replacement measured value, as shown in Figure 8.When being illustrated in boiler liquid level among the figure and reaching 150 millimeters, the sensor of measuring boiler liquid level breaks down.

Have only a sensor to break down at any one time at most, because the sampling interval is very short, whole process fault detection can be described below: with No. 1 sensor is example, when this sensor breaks down, whether the inventive method can surpass specified value according to the absolute value of the difference of the measured value of No. 1 sensor and predicted value is judged, this process be exactly fault diagnosis detection with separate.Fault has taken place if determine No. 1 sensor, then No. 1 sensor data of being sent to subsequent network are just replaced by the predicted value of No. 1 sensor, this is because the input of No. 1 neutral net is other sensing data except that No. 1 sensor, they do not have fault, so the output of No. 1 network is normal, the output that substitutes the sensor that breaks down with it is fine.Fig. 8 is the design sketch after the boiler liquid level sensor breaks down during at 150mm.The actual boiler liquid level when controlling with the neural network prediction value of training and the difference of detected value are less than 1 millimeter.

Claims (2)

1. boiler sensor fault diagnosis and fault-tolerant integral method based on an Intrnmet net is characterized in that the concrete control procedure of this method is as follows:

Step 1, image data: the data corresponding voltage value x (1) that pick-up transducers is measured, x (2) ..., x (n);

Step 2, packet, determine that input and output mode is right:

I. determine the quantity m of each input and output mode centering input data, i.e. the quantity of observation, m value is more definite according to the experimental data and the measurement result of test of many times acquisition;

II. for single-sensor, n the data of being gathered are divided into the k group, k=n-(m-1), every group has m+1 value, wherein preceding m value is as the input value of neutral net input node, m+1 value forms sample as the neutral net output after training, promptly since the 1st data, with preceding m the data x (1) of sensor output, x (2), ..., x (m) is as the input of neutral net, forms the 1st group of sample with m+1 the data x (m+1) of sensor output as the output of neutral net; Since the 2nd data, with m data x (2), x (3) ..., x (m+1) is as the input of neutral net, forms the 2nd group of sample with x (m+2) as the output of neutral net; The rest may be inferred, the sensor output sequence can be divided into the k group, every group of m+1 data, and it is right to form k input and output mode; The data of grouping are used for the single neutral net of off-line training, this moment p=m, q=1, training obtains each weights;

For multisensor, the quantity of setting sensor is N, and the number of neutral net also is N so, be N neutral net parallel connection, each sensor is gathered n data, equally n the data of being gathered is divided into k group, k=n-(m-1), every group has m+1 value, wherein preceding m input value that is worth as each neutral net input node, m+1 output that is worth as each neutral net, like this, the input data are total up to N * m, and the output data are total up to N;

Step 3, with the grouping data be used for the off-line training neutral net, obtain each weights by the BP network learning procedure; The concrete steps of BP network learning procedure are as follows:

(1) each connection weight W is given in initialization _Ij, V _JtAnd threshold value is tried to gain θ _j, γ _t, and give random value between [1 ,+1];

(2) picked at random one pattern is right $A_k=[a_1^k,a_2^k,...,a_n^k],$ $Y_k=[y_1^k,y_2^k,...,y_q^k],$ Offer network; Use input pattern $A_k=[a_1^k,a_2^k,...,a_n^k],$ Connection weight W _IjWith threshold value θ _jCalculate each neuronic input s of intermediate layer _j(activation value) uses s then _jObtain the output b in intermediate layer by activation primitive _j, i.e. formula (3):

$b_j=f\left(s_j\right)=\frac{1}{1+\exp (-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{W}_{\mathrm{ij}}\·a_i+{\mathrm{\θ}}_j)},(j=\mathrm{1,2},...,p)---\left(3\right)$

(3) the output b in usefulness intermediate layer _j, connection weight W _JtAnd threshold gamma _tCalculate the input l of each unit of output layer _t(activation value) uses l then _tCalculate the output c of each unit of output layer by activation primitive _t, i.e. formula (5):

$c_t=f\left(l_t\right)=\frac{1}{1+\exp (-\underset{j=1}{\overset{p}{\mathrm{\Σ}}}{V}_{\mathrm{jt}}\·b_j+{\mathrm{\γ}}_t)},(t=\mathrm{1,2},...,q)---\left(5\right)$

V in the formula _JtJ unit ,-intermediate layer to output layer t unit connection weight;

γ _t-output layer t cell threshode;

F-S type activation primitive;

(4) with wishing output mode $Y_k=[y_1^k,y_2^k,...,y_q^k],$ The actual output of network c _tCalculate the correction error d of each unit of output layer _t ^k, i.e. formula (6):

$d_t^k=(y_t^k-c_t^k)\·f^{\′}\left(l_t^k\right)=(y_t^k-c_t^k)\·c_t^k(1-c_t^k),t=\mathrm{1,2},...,q,k=\mathrm{1,2},...---\left(6\right)$

Y in the formula _t ^k-desired output;

c _t ^k-actual output;

F '-to the output layer function derivative;

(5) use V _Jt, d _t, b _jCalculate the correction error e in intermediate layer _j ^k, i.e. formula (7);

$e_j^k=(\underset{t=1}{\overset{q}{\mathrm{\Σ}}}{V}_{\mathrm{jt}}\·d_t^k)\·f^{\′}\left(s_j^k\right)=(\underset{t=1}{\overset{q}{\mathrm{\Σ}}}{V}_{\mathrm{jt}}\·d_t^k)\·b_j\·(1-b_j),(j=\mathrm{1,2}...p,k=\mathrm{1,2},...,m)---\left(7\right)$

Use d _t ^k, b _j, V _JtAnd γ _tCalculate next time intermediate layer and the new connection weight between the output layer, i.e. formula (8) and (9):

$V_{\mathrm{jt}}(N+1)=V_{\mathrm{jt}}\left(N\right)+\mathrm{\α}\·d_t^k\·b_j\⇒\mathrm{\Δ}{V}_{\mathrm{jt}}=\mathrm{\α}\·d_t^k\·b_j^k---\left(8\right)$

${\mathrm{\γ}}_t(N+1)={\mathrm{\γ}}_t\left(N\right)+\mathrm{\α}\·d_t^k\⇒\mathrm{\Δ}{\mathrm{\γ}}_t=\mathrm{\α}\·d_t^k---\left(9\right)$

B in the formula _j ^kThe output of j unit ,-intermediate layer;

d _t ^kThe correction error of-output layer;

J=1,2 ..., p t=1,2 ..., q k=1,2 ..., m 0＜α＜1 (learning coefficient);

(6) by e _j ^k, a _i ^k, W _IjAnd θ _jCalculate next time input layer and the new connection weight between the intermediate layer, i.e. formula (10) and (11):

$W_{\mathrm{ij}}(N+1)=W_{\mathrm{ij}}\left(N\right)+\mathrm{\Δ}{W}_{\mathrm{ij}}=W_{\mathrm{ij}}\left(N\right)+\mathrm{\β}\·e_j^k\·a_i^k\⇒\mathrm{\Δ}{W}_{\mathrm{ij}}=\mathrm{\β}\·e_j^k\·a_i^k---\left(10\right)$

${\mathrm{\θ}}_j(N+1)={\mathrm{\θ}}_j\left(N\right)+\mathrm{\Δ}{\mathrm{\θ}}_j={\mathrm{\θ}}_j\left(N\right)+\mathrm{\β}\·e_j^k\⇒\mathrm{\Δ}{\mathrm{\θ}}_j=\mathrm{\β}\·e_j^k---\left(11\right)$

E in the formula _j ^kThe correction error of j unit ,-intermediate layer;

I=1,2 ..., n＜0＜β＜1 (learning coefficient);

(7) choose next mode of learning to offering network, turned back to for the 3rd step, until whole m pattern to having trained;

(8) right from pattern of m mode of learning centering picked at random again, turned back to for the 3rd step, until network global error function E less than predefined limits value;

Step 4, fault diagnosis: for the single-sensor situation, in promptly any K moment, the neutral net input value is the data of preceding K-m pick-up transducers measurement constantly, be x (K-m), and x (K-m+1) ..., x (K-1), the neutral net output valve is that predicted value is

Work as predicted value With the difference of the measured value of sensor during greater than setting threshold, or when failing to obtain the measured value of sensor owing to time delay, the data-bag lost of network, then failure judgement takes place; For the multisensor situation, suppose that arbitrary moment has only a sensor to break down at most, number of sensors is N, neutral net input data are:

[x ₁(K-m), x ₁(K-m+1) ..., x ₁(K-1)] be first grouped data,

[X ₂(K-m), x ₂(K-m+1) ..., x ₂(K-1)] be second grouped data,

......、

[X _N(K-m), x _N(K-m+1) ..., x _N(K-1)] be N grouped data,

Neutral net output valve i.e. K predicted value constantly is

I=1,2 ..., N, the difference of the output of i neutral net and the measured value of i sensor is during greater than setting threshold, or owing to the time delay of network, when data-bag lost fails to obtain the measured value of i sensor, then failure judgement generation;

Step 5, fault-tolerant:, use for the single-sensor situation

Replace the output of sensor; For the multisensor situation, i neutral net output Replace the output of i sensor.

2. realize as claimed in claim 1 based on the boiler sensor fault diagnosis of Internet net and the device of fault-tolerant integral method, it is characterized in that this device comprises that remote fault diagnosis tolerant system, Internet net, host computer, slave computer, on-the-spot sensing become and send part and control section, wherein:

On-the-spot sensing becomes send part: comprise temperature, liquid level, flow, pressure detecting instrument, be responsible for parameter acquisition and transmission;

Slave computer: be responsible for the signal A/D conversion of gathering, and signal is sent to host computer; Control signal D/A conversion, the control steam generator system;

Host computer: collect local slave computer data, and give remote fault diagnosis and fault-tolerant control system by the Internet network transmission, the control signal by Internet net receiving remote fault diagnosis and fault-tolerant control system send sends slave computer to;

The Internet net: remote fault diagnosis and fault-tolerant control system receive and send signal by the Internet net;

Control section: receive the fault-tolerant control signal that transmits by slave computer, control is realized at the scene;

Its course of work is: in the on-the-spot installation and measuring instrument of boiler, instrumentation is delivered to slave computer with the signal of gathering, regularly will gather signal is sent to host computer to slave computer, host computer passes to the remote fault diagnosis fault-tolerant control system to the data of accepting by the Internet net, carry out fault diagnosis and robust parsing, then control signal is beamed back host computer by the Internet net, host computer sends to slave computer to signal again, is transmitted control signal to control section by slave computer and finishes control.

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