CN102765643A - Elevator fault diagnosis and early-warning method based on data drive - Google Patents
Elevator fault diagnosis and early-warning method based on data drive Download PDFInfo
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Abstract
The invention relates to the field of elevators. In order to early discover and diagnose A elevator fault, the invention adopts the technical scheme that an elevator fault diagnosis and early-warning method based on data drive is achieved by means of a remote service center, a fault diagnosis and prediction terminal and an elevator controller, and the method comprises the steps as follows: firstly, elevator fault data are mined to obtain characteristic information in an elevator fault data stream, and the mined result is stored in an elevator fault case base of the fault diagnosis and prediction terminal; secondly, an elevator fault knowledge base on the fault diagnosis and prediction terminal is updated by the elevator fault case base; thirdly, the case retrieval is carried out on the characteristic of a new elevator fault problem, and the fault diagnosis is carried out on the elevator system by adopting the fault diagnosis method based on the case-base reasoning; and finally information with the characteristic that is most similar with that of the new elevator fault problem is acquired through retrieval of the knowledge or the case in the elevator fault knowledge base to solve the diagnosis problem. The method is mainly suitable for manufacturing and designing image sensors.
Description
Technical field
The present invention relates to the elevator field, specifically, relate to elevator faults diagnosis and method for early warning based on data-driven.
Background technology
Because the domestic elevator quantity of potential safety hazard that exists increases rapidly in recent years, only elevator is safeguarded through maintainer's experience or manual of maintenance have that efficient is low, poor accuracy and problem such as diagnosis afterwards often, can not satisfy the needs of elevator safety.Elevator needs a kind of intelligent trouble diagnosis and forewarn system to guarantee system safety operation.
Domestic solution elevator safety problem is mainly through two approach: the one, and the trouble diagnosing after fault takes place, the 2nd, the maintaining that the maintainer is regular.And the fault diagnosis technology of widespread use at present mainly contains expert system, fuzzy reasoning, neural network etc.But these technology depend critically upon expertise, and expertise obtains difficulty becomes the bottleneck that trouble diagnosing is implemented.In addition, most of method for diagnosing faults all can not provide the failure prediction function, and the passive-type diagnosis can't stop the generation of fault, can only be fixed against the maintenance of elevator periodical maintenance.Not only cost is high for the indefinite periodic maintenance of purpose, efficient is low, and relies on hand inspection also to be difficult to find the potential safety hazard of elevator.
Summary of the invention
The present invention is intended to overcome the deficiency of prior art; Realize the early detection and the diagnosis of elevator faults; For achieving the above object, the technical scheme that the present invention takes is, based on the elevator faults diagnosis and the method for early warning of data-driven; Realize by means of remote service center, fault diagnosis and fault prediction terminal and electric life controller, comprise the steps:
At first real-time elevator faults data are excavated the characteristic information that obtains in the elevator faults data flow, and will excavate in the elevator faults case library that the result is kept at the fault diagnosis and fault prediction terminal, as the source of elevator faults knowledge base; Utilize the elevator faults case library that the elevator faults knowledge base on the fault diagnosis and fault prediction terminal is upgraded then; Calculate by the similarity coupling; Realize upgrading in time of elevator faults knowledge base; Carry out the case retrieval to the feature of new elevator faults problem again; Employing is carried out fault diagnosis based on the method for diagnosing faults of reasoning by cases to elevator device: by retrieval elevator faults knowledge base knowledge or case; Obtain to have the information of similar features, solve diagnosis problem with new elevator faults problem;
In addition; Utilize the elevator faults discriminator device on the remote service center; Elevator faults data flow to obtaining is carried out cluster analysis, corresponding elevator faults data flow and elevator faults type association got up, and with this elevator faults data flow and corresponding failure type training classifier; Segregator is tested with corresponding fault type through another group elevator faults data flow again, with the correctness of the segregator after the checking training; Remote service center is brought in constant renewal in segregator; And up-to-date segregator downloaded in the local fault diagnosis and fault prediction terminal; Local fault diagnosis and fault prediction terminal is gathered the elevator data flow in real time and it is imported segregator, is flowed and is had now the elevator faults data flow by segregator output real time data and carry out similarity degree relatively, and similarity degree is big more; The possibility that fault of the same race occurs is big more, carries out the elevator faults prediction according to this.
It is on the fault diagnosis and fault prediction terminal, to carry out that employing is carried out trouble diagnosing based on the method for diagnosing faults of reasoning by cases to elevator device, and the step refining of going forward side by side is following steps:
(1) elevator faults knowledge base: the set that is elevator faults diagnostic knowledge, experience; Mainly provide by the expert; Comprise the classified information of elevator essential information, elevator faults and the various key feature attributes and the weights thereof of variety classes fault needs, and make up elevator faults case library and sign variable storehouse according to this;
(2) set up the elevator faults case library: the maintainer fills in the various information about elevator faults according to the historical data that comprises elevator faults daily record and maintenance daily record, and stores case on this basis and produce new case;
(3) set up the sign variable storehouse: the fault type traffic flow information that collects during the storage Lift out of order, each parameter of elevator operation when promptly fault takes place;
(4) set up rule base: store the interrelated information between the various elevator faults types; Be to fault case storehouse association rule algorithm; Carry out data mining, from numerous elevator faults case informations, extract profound, tacit knowledge; Be used for the elevator faults diagnosis, the guide maintenance personnel make the maintenance measure of response;
(5) inference system: form by case retrieval, case coupling, case adjustment; Be specially: through the elevator faults case library is carried out case retrieval seek one or more with when the most similar case of prior fault, the searching algorithm of using has the template check, conclude retrieve, nearest neighbor search; Based on the case generation solution that retrieves and through the case correction solution that has generated is adjusted then, the method for adjustment has transformation approach, replacement method, specific objective to drive method;
(6) case study: according to maintainer's feedback information; It is multiplexing that the elevator faults case library is carried out case, if promptly this scheme can solve the fault that runs into then preserve the maintenance suggestion in the elevator faults case library, otherwise is saved in the fault case storehouse after this scheme made amendment; Continuous like this new knowledge and the old knowledge of improvement obtained; Form new maintenance program, and add in the case library, make case library constantly obtain expansion and perfect.
Case is retrieved concrete performing step:
(1) gather the elevator faults data flow, characteristic information extraction and according to the taxonomic structure index, preliminary search goes out to meet the case kind of characteristic information.
(2) according to the kind of fault case failure message eigenwert and elevator faults knowledge base are mated.
(3) calculate according to improved European algorithm; Calculate the matching degree of all cases in this target case and the initial matching casebook; And sort according to the size of matching degree, former case that output and target case are mated are most accomplished the case matching process; At last, show case coupling details, and prepare for the case correction.
The generative process of segregator comprises data preprocessing module, characteristic extracting module and segregator generation module; Wherein data preprocessing module adopts and comprises normalisation, variance reduction step, noise datas such as the abnormal data in the responsible rejecting data, redundant data; Characteristic extracting module adopts principal component analysis (PCA), PLS, is responsible for reduced data stream, improves training effectiveness; The segregator generation module also comprises neural network, SVMs submodule.
Technical characterstic of the present invention and effect:
Data mining identifies actv., novelty, type potentially useful and that finally can be understood from data.The key of trouble diagnosing and matter of utmost importance are exactly fault recognition, to the process of diagnosing malfunction just fault type obtain and the process of fault recognition.Consider the unique advantage of data mining technology aspect knowledge acquisition, it is practicable introducing this technology in fault diagnosis field.Can utilize historical data to excavate wherein potential rule,, have actual reference for trouble diagnosing provides decision-making foundation.
Trouble diagnosing and forewarn system based on data mining have the following advantages:
(1) breaks through the elevator diagnostic knowledge and obtained difficulty, bottleneck that knowledge quantity is few.Can automatically obtain diagnostic experience and need not artificial the summary and input, improve diagnosis efficiency and accuracy greatly, reduce the diagnosis cost.
(2) for fault larger, that relate to a plurality of variablees, use diagnostic method to solve to single part, utilize data mining technology that the elevator operating data is carried out bulk analysis and can effectively diagnose.
(3) not only can corresponding fault solution can also be provided for the maintainer finds fault cause and position.
(4) can monitor in real time the elevator operating data, obtain the similarity degree of real time data stream and fault type, thereby realize the early detection and the early warning of elevator faults through segregator.
(5) this system has self-learning capability, constantly learns new fault data and forms new diagnostic knowledge, and along with the continuous increase of fault data, the trouble diagnosibility of system can constantly strengthen.
(6) for preventive maintenance the basis is provided.Preventive maintenance based on failure prediction has reduced blindness, and elevator is keeped in repair arriving effectively of best break down maintenance time, has not only reduced frequency of maintenance and cost, and the efficient of maintenance also improves greatly.
Description of drawings
Fig. 1 is based on the elevator faults diagnosis and forewarn system integral structure figure of data-driven.
Fig. 2 is based on the elevator faults diagnosis framework figure of reasoning by cases.
Fig. 3 case information is represented scheme drawing.
Fig. 4 elevator faults case search strategy diagram of circuit.
Fig. 5 is based on the elevator faults prediction diagram of circuit of segregator.
Fig. 6 elevator long distance servicing center segregator generates scheme drawing.
Fig. 7 is based on the classifier design scheme drawing of BP neural network.
The specific embodiment
The objective of the invention is to propose a kind of elevator faults diagnosis and forewarn system, realize trouble diagnosing and failure prediction efficiently accurately based on data-driven.
Existing elevator faults diagnostic technology exists expertise to obtain problems such as difficulty, diagnosis efficiency are low, cost height; To these problems; The method for diagnosing faults that the present invention uses based on reasoning by cases carries out trouble diagnosing to elevator device, in the elevator faults case library, retrieves the optimum matching case behind the et out of order, and safeguards according to the fault cause in the case information, position of fault and fault solution; The simultaneous faults case library can carry out the maintenance of case library automatically; Comprise the increase case, merge case, the redundant case of deletion etc., thereby possess very strong learning ability.
Present most of fault localization system lacks the failure prediction function; The present invention analyzes the elevator historical data by data mining technology, concludes the data flow that sums up corresponding specific fault, takes all factors into consideration expertise and data flow; Data flow when elevator is moved compares with the known fault data flow in real time; And both similarity degree quantized to calculate, after similarity acquires a certain degree, can propose fault pre-alarming, thereby accomplish the failure prediction function elevator device.This system obtains the general parameters of elevator operation and automatically operating data is analyzed, and has broken through the bottleneck that expert system is obtained difficulty, has the diagnosis efficiency height, cost is low and can realizes the advantage of failure prediction function.The present invention need not installed additional sensors additional, applicable to the elevator of various different brands, has good application prospects and economic value, and this System and method for also has very high reference value to the trouble diagnosing of other field.
The present invention utilizes the elevator faults data mining technology; Designed a kind of elevator faults diagnostic system; This system is constantly to collecting data analysis from elevator device; The ability in knowledge acquisition of dependence data mining technology forms the trouble diagnosing knowledge of elevator device automatically and efficiently, has solved expertise and has obtained difficult problem, has overcome the diagnotic technical bottleneck of present elevator faults.On the basis of this system architecture, propose a kind of elevator faults diagnostic method based on reasoning by cases then, the knowledge of utilizing said method to form is diagnosed.
In addition; On the basis of this system architecture, add failure prediction function based on segregator; Can monitor the elevator data flow in real time; And utilize segregator that these data flow are analyzed and discerned, calculate the similarity size and the trend of current data stream and fault data stream, and then realize the early detection and the diagnosis of elevator faults.
The present invention designs a kind of elevator faults diagnosis and forewarn system based on data-driven by data mining technology, has the trouble diagnosing and the forecast function of enhancing.
The present invention at first excavates the characteristic information that obtains in the elevator faults data flow through data mining algorithm to real-time elevator faults data, and will excavate the result and be kept in the elevator faults case library, as the source of elevator faults knowledge base.Utilize the elevator faults case library that the elevator faults knowledge base is upgraded then, calculate, realize upgrading in time of elevator faults knowledge base through the similarity coupling.Carry out the case retrieval to the characteristic of new problem again,, obtain to have the information of similar features, be used to solve diagnosis problem with new elevator faults problem through the knowledge or the case of retrieval elevator faults knowledge base.
In addition; Be designed for elevator faults discriminator device; Group data stream to obtaining carries out cluster analysis, corresponding data flow and fault type associated, and with this data flow and corresponding failure type training classifier; Through another group data stream and corresponding fault type segregator is tested again, with the correctness of the segregator after the checking training.Remote service center is brought in constant renewal in segregator; And up-to-date segregator downloaded in the local diagnosis terminal; Local diagnosis terminal is gathered the elevator data flow in real time and data flow is imported segregator, and by segregator output real time data stream and the similarity degree that has fault data stream now, similarity is big more; The possibility that fault of the same race occurs is big more, can carry out the elevator faults prediction successively.
Below in conjunction with accompanying drawing the present invention is made further detailed description.
Core of the present invention is through elevator operating data stream is analyzed; The characteristic information of data flow when excavating fault; Find and the corresponding data flow of elevator faults type; And be translated into expertise, and deposit elevator faults diagnosis cases storehouse in, adopt method for diagnosing faults that elevator device is carried out trouble diagnosing again based on reasoning by cases.In addition; Design error failure data flow classification device; Can carry out real-time grading to elevator current data stream in real time; And it is big or small with elevator faults data flow similarity to calculate current data stream through the similarity algorithm based on distance, and similarity major break down possibility more is big more, carries out failure prediction according to this similarity trend or through the method that threshold value is set.
Referring to Fig. 1, diagnosis comprises three parts with forewarn system based on the elevator faults of data-driven: remote service center, fault diagnosis and fault prediction terminal and electric life controller.
When fault takes place; Each parameter in diagnostic code in the electric life controller register system and the current elevator device, as: towing machine rotating speed, car acceleration/accel, frequency converter voltage, inverter current, flat bed signal, the signal of rushing to summit, hit end signal, door machine signal etc.And import diagnostic code and parameter current into local diagnostic platform in the lump.During normal the operation, only need send to local diagnosis terminal to current system parameter in real time for failure prediction.
In the local diagnosis terminal elevator faults diagnosis and prediction software and SQLServer2005 database software are set; After receiving elevator faults sign indicating number and parameter current, will extract the eigenwert of this fault type; In case library, seek the optimum matching case then according to this, fault cause and the solution with this failure message and coupling sends the far-end maintainer to through portable terminals such as Internet or mobile phones again; If the matching degree of when the best case of coupling, finding current and best case is then regarded as new fault type with this fault, and current failure message is sent to remote service center less than threshold value.On the other hand, the classifier modules that the integrated utilization com component is write in the elevator faults diagnosis and prediction software, the forecast function of completion elevator faults.
Data flow when remote service center is responsible for collecting all elevator device faults; And be stored in segregator and case library in the servicing center with the data flow under these faulty conditions training, bring in constant renewal in segregator and case library make fault type recognition with diagnose accurate further; What remote service center was regular downloads to local diagnosis terminal to up-to-date segregator and case library, and the elevator faults early warning information is made response.
Referring to Fig. 2, mainly comprise four data banks, inference system and a case study module based on the elevator faults diagnosis framework of reasoning by cases.Each several part specifically describes as follows:
(1) knowledge base: the set of elevator faults diagnostic knowledge, experience; It is mainly provided by the expert; Comprise the classified information of elevator essential information, elevator faults and the various key feature attributes and the weights thereof of variety classes fault needs, and make up elevator faults case library and sign variable storehouse according to this.
(2) fault case storehouse: the maintainer fills in the various information about elevator faults according to elevator faults daily record and historical datas such as keeping in repair daily record, and stores case on this basis and produce new case.
(3) sign variable storehouse: the fault type traffic flow information that collects during Lift out of order, each parameter of elevator operation when promptly fault takes place.
(4) rule base: the interrelated information between the various elevator faults types.Be to fault case storehouse association rule algorithm, carry out data mining, from numerous elevator faults case informations, extract profound, tacit knowledge, be used for the elevator faults diagnosis, the guide maintenance personnel make the maintenance measure of response.
(5) inference system: the core of diagnostic system, form by case retrieval, case coupling, case adjustment.Through the elevator faults case library is carried out case retrieval seek one or more with when the most similar case of prior fault, the searching algorithm that possibly use has template check, conclusion retrieval, nearest neighbor search etc.Generate solution and through the case correction method of the solution adjustment that generated is had transformation approach, replacement method, specific objective according to the case that retrieves then and drive method, most case adjustment is all accomplished through man-machine interaction mode.Inference system has determined the height of diagnosis efficiency, realizes from existing casebook, finding the case the most similar with current problem, and corresponding fault solution is provided.
(6) case study: according to maintainer's feedback information; It is multiplexing that the elevator faults case library is carried out case; If promptly this scheme can solve the fault that runs into then preserve the maintenance suggestion in the elevator faults case library, otherwise be saved in the fault case storehouse after this scheme made amendment.Continuous like this new knowledge and the old knowledge of improvement obtained forms new maintenance program, and adds in the case library, is that case library constantly obtains expansion and perfect.
Referring to Fig. 3; Each case in the case library all is made up of case essential information, fault cause and location and fault solution; Electric life controller provides essential informations such as fault data stream elevator signal in the diagnostic procedure; Diagnostic system is then analyzed according to these information, returns information such as fault cause and location and fault solution.
Referring to Fig. 4, case retrieval is the key of whole elevator faults diagnostic process based on reasoning by cases, below is concrete performing step:
(1) gather the fault data stream of fault elevator, characteristic information extraction and according to the taxonomic structure index, preliminary search goes out to meet the case kind of characteristic information.
(2) according to the kind of fault case failure message eigenwert and casebook are mated.
(3) calculate according to improved European algorithm; Calculate the matching degree of all cases in this target case and the initial matching casebook; And sort according to the size of matching degree, former case that output and target case are mated are most accomplished the case matching process.At last, show case coupling details, and prepare for the case correction.
Following according to each case structure attribute Jacobian matrix in the elevator faults case library:
A wherein
IjRepresent j attribute of i case.Remember j attribute aviation value be B
j, then:
Note intermediate variable M
Ij
Order again:
Improvement Euclidean distance between target case and source case is:
W in the formula
iThe weighted value that provides for expertise.d
TiValue big more, show that the distance between target case and the source case is more little, similarity is high more, calculates the minimum source case of distance in the retrieving and diagnoses.
Improved European algorithm is introduced intermediate variable M on the basis of the European algorithm of tradition
Ij, promptly increased a normalized process of property value, can prevent effectively that some attribute value in the same case is excessive, cause result for retrieval to depart from actual situation and occur.
Referring to Fig. 5; Through segregator the elevator faults data flow is discerned and calculation of similarity degree; Finally can obtain the fault similarity development tendency or with the comparative result of threshold value; In order to guarantee it is other accuracy, segregator has remote service center to carry out regular update, finally accomplishes the failure prediction function of elevator.
Referring to Fig. 6, the generation of segregator comprises two stages (training stage and testing stage).The fault data stream that is used for the segregator generation is divided into two parts, and wherein 2/3rds data are used the training stage, and 1/3rd data are used for testing stage.The preliminary segregator that generates of training stage is verified to guarantee its accuracy the segregator that has generated at testing stage.
The generative process of segregator comprises data preprocessing module, characteristic extracting module and segregator generation module; Wherein data preprocessing module is responsible for rejecting noise datas such as abnormal data in the data, redundant data; Characteristic extracting module is responsible for reduced data stream, improves training effectiveness.The segregator generation module comes down to the module that SVMs, neural network etc. have the nonlinear function analog functuion.Data preprocessing module need be used statistics and mathematical tool comprises normalisation, variance reduction etc., and the characteristic processing module possibly used mathematical methods such as principal component analysis (PCA), offset minimum binary.
Referring to Fig. 7, in the segregator generation module, adopt the BP neural network, the node number of definition input layer is 2, and output layer node number is 1, and the hidden layer node number is 6, uses Logsig type transfer function, representes as follows:
Weights are revised formula:
W
sq(t+1)＝W
sq(t)+η(t)δ
qy
s+αΔW
sq(t)
Weights wherein, η is a gain term, δ
qBe error term, y
sBe the output of node s node, the weights of α for setting.W
Sq(t) be the t time iteration weights.This segregator is realized under Visual C++ environment.Input sample set and cooresponding training objective collection directly are stored in the SQL database, to guarantee the commonality of data.Classifier modules is designed to com component, when needing this assembly is called.
Claims (4)
1. elevator faults diagnosis and method for early warning based on a data-driven is characterized in that, realize by means of remote service center, fault diagnosis and fault prediction terminal and electric life controller, comprise the steps:
At first real-time elevator faults data are excavated the characteristic information that obtains in the elevator faults data flow, and will excavate in the elevator faults case library that the result is kept at the fault diagnosis and fault prediction terminal, as the source of elevator faults knowledge base; Utilize the elevator faults case library that the elevator faults knowledge base on the fault diagnosis and fault prediction terminal is upgraded then; Calculate by the similarity coupling; Realize upgrading in time of elevator faults knowledge base; Carry out the case retrieval to the feature of new elevator faults problem again; Employing is carried out fault diagnosis based on the method for diagnosing faults of reasoning by cases to elevator device: by knowledge or the case in retrieval elevator faults knowledge base or the interim elevator faults case library; Obtain to have the information of similar features, solve diagnosis problem with new elevator faults problem;
In addition; Utilize the elevator faults discriminator device on the remote service center; Elevator faults data flow to obtaining is carried out cluster analysis, corresponding elevator faults data flow and elevator faults type association got up, and with this elevator faults data flow and corresponding failure type training classifier; Segregator is tested with corresponding fault type through another group elevator faults data flow again, with the correctness of the segregator after the checking training; Remote service center is brought in constant renewal in segregator; And up-to-date segregator downloaded in the local fault diagnosis and fault prediction terminal; Local fault diagnosis and fault prediction terminal is gathered the elevator data flow in real time and it is imported segregator, is flowed and is had now the elevator faults data flow by segregator output real time data and carry out similarity degree relatively, and similarity degree is big more; The possibility that fault of the same race occurs is big more, carries out the elevator faults prediction according to this.
2. elevator faults diagnosis and method for early warning based on data-driven as claimed in claim 1; It is characterized in that; It is on the fault diagnosis and fault prediction terminal, to carry out that employing is carried out trouble diagnosing based on the method for diagnosing faults of reasoning by cases to elevator device, and the step refining of going forward side by side is following steps:
(1) elevator faults knowledge base: the set that is elevator faults diagnostic knowledge, experience; Mainly provide by the expert; Comprise the classified information of elevator essential information, elevator faults and the various key feature attributes and the weights thereof of variety classes fault needs, and make up elevator faults case library and sign variable storehouse according to this;
(2) set up the elevator faults case library: the maintainer fills in the various information about elevator faults according to the historical data that comprises elevator faults daily record and maintenance daily record, and stores case on this basis and produce new case;
(3) set up the sign variable storehouse: the fault type traffic flow information that collects during the storage Lift out of order, each parameter of elevator operation when promptly fault takes place;
(4) set up rule base: store the interrelated information between the various elevator faults types; Be to fault case storehouse association rule algorithm; Carry out data mining, from numerous elevator faults case informations, extract profound, tacit knowledge; Be used for the elevator faults diagnosis, the guide maintenance personnel make the maintenance measure of response;
(5) inference system: form by case retrieval, case coupling, case adjustment; Be specially: through the elevator faults case library is carried out case retrieval seek one or more with when the most similar case of prior fault, the searching algorithm of using has the template check, conclude retrieve, nearest neighbor search; Based on the case generation solution that retrieves and through the case correction solution that has generated is adjusted then, the method for adjustment has transformation approach, replacement method, specific objective to drive method;
(6) case study: according to maintainer's feedback information; It is multiplexing that the elevator faults case library is carried out case, if promptly this scheme can solve the fault that runs into then preserve the maintenance suggestion in the elevator faults case library, otherwise is saved in the fault case storehouse after this scheme made amendment; Continuous like this new knowledge and the old knowledge of improvement obtained; Form new maintenance program, and add in the case library, make case library constantly obtain expansion and perfect.
3. elevator faults diagnosis and method for early warning based on data-driven as claimed in claim 1 is characterized in that case is retrieved concrete performing step:
(1) gather the elevator faults data flow, characteristic information extraction and according to the taxonomic structure index, preliminary search goes out to meet the case kind of characteristic information.
(2) according to the kind of fault case failure message eigenwert and elevator faults knowledge base are mated.
(3) calculate according to improved European algorithm; Calculate the matching degree of all cases in this target case and the initial matching casebook; And sort according to the size of matching degree, former case that output and target case are mated are most accomplished the case matching process; At last, show case coupling details, and prepare for the case correction.
4. elevator faults diagnosis and method for early warning based on data-driven as claimed in claim 1; It is characterized in that; The generative process of segregator comprises data preprocessing module, characteristic extracting module and segregator generation module; Wherein data preprocessing module adopts and comprises normalisation, variance reduction step, noise datas such as the abnormal data in the responsible rejecting data, redundant data; Characteristic extracting module adopts principal component analysis (PCA), PLS, is responsible for reduced data stream, improves training effectiveness; The segregator generation module also comprises neural network, SVMs submodule.
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