CN103481943B - A kind of fault localization system for self-propelled hydraulic bogie hydrostatic steering system - Google Patents

Abstract

The invention discloses a kind of fault localization system for self-propelled hydraulic bogie hydrostatic steering system, this fault localization system is monitored hydrostatic steering system according to transfinite sensory information and the single-wheel error sensing information of Real-time Collection, by Real-Time Monitoring hydrostatic steering system to determine the hydrostatic steering system loop local diagnosis whether started based on ARX model and FCM cluster.The result of hydrostatic steering system loop local diagnosis informs chaufeur by Vehicular display device.Adopt fault localization system of the present invention to be conducive to improving reliability and the safety of self-propelled hydraulic bogie, guarantee the safety of field man.

Description

A kind of fault localization system for self-propelled hydraulic bogie hydrostatic steering system

Technical field

The present invention relates to a kind of trouble diagnosing of hydrostatic steering system, more particularly, refer to a kind of fault localization system based on active autoregressive model (ARX model) and fuzzy C-means clustering (FCM cluster) being applicable to hydrostatic steering system in self-propelled hydraulic bogie, this fault localization system is stored in each controller of hydrostatic steering system.

Background technology

Large-scale self-propelled hydraulic bogie, or claim hydraulic powered plate transport trolley to be the special rubber-tyred large-scale engineering machinery of a class, typically refer to payload ratings more than 50 tons, have take turns hydraulic-driven more, turn to, self-level(l)ing and lifting apparatus for work, and each wheel all can the special transportation vehicle of independent steering.

In October, 2010, Chemical Industry Press, " large-scale self-propelled hydraulic bogie " that Zhao Jingyi writes.In chapter 2, the 57th page of structure describing steering control system of this book.The block diagram of this structure as shown in Figure 1, in figure, the function that realizes separately of three controllers for convenience of description, the controller receiving handling device information is called master controller, the controller sent control information to multiple apportioning valve is called field controller, the controller receiving multiple angular-motion transducer data is called data acquisition controller; Master controller carries out kinematic synthesis and cooperation control according to the angular signal of the chaufeur input that handling device receives, and calculates each wheel steering angle in real time, so wheel steering angle sends to field controller as with reference to input by CAN; Field controller carrys out the size of control ratio valve opening according to each wheel steering angle reference input, to drive Hydraulic Cylinder steering hardware, thus overcomes steering resisting torque and realization turns to; Angular-motion transducer measures the corner of each wheel in real time, and sends information by data acquisition controller and CAN.Fluid-link steering adopts electro-hydraulic proportional control system, the corner respectively hung by angular-motion transducer closed loop control, realize to hang the accurate control that turns to and tyre revolution to time approximate pure rolling.

In Practical Project, the division of control task is carried out in the many employings of Control System Design of self-propelled hydraulic bogie with physical distribution, as shown in Figure 2, namely master controller completes bogie walking, turns to and the kinematic synthesis of the function such as leveling and cooperation control, the desired speed of each wheel of real-time resolving, turns over angle and hangs lifting altitude; Field controller according to the desired signal of master controller real-time resolving complete respectively wheel in range of control walking, turn to and the closed loop control of the function such as to hoist.Wherein, AA represents primary scene controller, BB represents secondary scene controller, CC represents the 3rd field controller, DD represents the 4th field controller; Rectangle frame represents wheel, and every 2 wheels form a train, and two circle represents the rotating shaft of steering hardware, and black thick line represents fieldbus (as CAN, Modbus bus, 1553B bus).Each train loop is numerous, trouble point or failsafe link many; And under large-scale self-propelled hydraulic bogie is in the harsh environments such as outdoor, dust for a long time, therefore very easily cause breakdown of equipment, gently then affect job schedule, serious even jeopardizes field operation personal security, causes huge economic loss.

Summary of the invention

The object of the invention is to propose a kind of fault localization system for self-propelled hydraulic bogie hydrostatic steering system, this system is extracted by active autoregressive model (ARX model) and turns to loop fault feature, carry out Fault Pattern Recognition by fuzzy C-means clustering (FCM cluster), thus diagnose out the fault turned in loop.

A kind of fault localization system for self-propelled hydraulic bogie hydrostatic steering system of the present invention, this fault localization system is stored in each controller of hydrostatic steering system, it is characterized in that: processing module (2) that this fault localization system includes sensor threshold value monitoring modular (1), threshold value transfinites, single-wheel tracking error monitoring modular (3), error transfinite processing module (4) and local diagnostic process module (5);

Sensor threshold value monitoring modular (1) first aspect is for receiving each train actual measurement sensory information second aspect according to the priority of time of reception by described in each be stored in the data buffer of each controller of hydrostatic steering system; The third aspect adopts slip mean filter method to described carry out filtering process, obtain sensor monitoring value fourth aspect is by sensor monitoring value export to threshold value to transfinite processing module (2); The actual measurement sensory information of each train include the direction information Y of the single-wheel that angular-motion transducer gathers _{turn to}, pressure sensor export load-sensitive pump discharge pressure P _pressure, the temperature T of hydraulic oil that exports of temperature sensor _{oil temperature};

Threshold value transfinites processing module (2) first aspect for receiving each train sensor monitoring value second aspect adopts out-of-limit condition to sensor monitoring value carry out judgement of transfiniting, obtain the result RS of threshold monitor ^cL; The third aspect is by RS ^cL=1 threshold monitor result exports to local diagnosis processing module (5); Fourth aspect is by RS ^cL=0 threshold monitor result exports to single-wheel tracking error monitoring modular (3);

Single-wheel tracking error monitoring modular (3) first aspect is according to the threshold monitor result RS received ^cL=0 to the direction information Y occurring the train CL that sensor threshold value transfinites _{turn to}compare, obtain turning to the tracking error E that transfinites ^cLwhat=R-Y, R represented wheel turns to input, and Y represents the response output of wheel; Second aspect according to wheel previous moment t-1 turn to the tracking error that transfinites calculate and turn to the variable quantity that transfinites under current time t the third aspect will export to error to transfinite processing module (4);

Error processing module (4) first aspect that transfinites turns to the variable quantity that transfinites for receiving under current time t second aspect is according to turning to loop out-of-limit condition single-wheel tracking error is monitored, obtains the single-wheel monitoring result under current time export; Wherein, δ > 0, δ is detection sensitivity threshold value; The third aspect will the single-wheel result that do not transfinite exports to controller; Fourth aspect will the single-wheel result that transfinites exports to local diagnosis processing module (5);

Local diagnosis processing module (5) first aspect is for receiving RS ^cLthe threshold monitor result of=1; Second aspect is used for receiving single-wheel to transfinite result; Third aspect foundation ARX model is respectively to RS ^cLthe threshold monitor result of=1 and the single-wheel result that transfinites process, obtain fault signature; Fourth aspect processes fault signature according to FCM cluster, obtains failure mode.

The advantage that the present invention is based on the fault localization system of ARX model and FCM cluster is:

1. according to the distributed nature of hydraulic pressure bogie physical distribution and control task, each field controller carries out trouble diagnosing to steering swivel system loop in range of control, thus reduce communication overhead unnecessary in diagnostic procedure.

2. based on the trouble diagnosing of ARX model and FCM cluster, use active autoregressive model (auto-regressivewithextrainputsmodel, be called for short ARX model) carry out the extraction of fault signature, ARX model method is a kind of seasonal effect in time series analysis method, by state of the system being agglomerated in its model parameter to the linear fit of respective sensor data, when fitting precision is higher, ARX model is accurate, can deeply, the moving law of intensively expression system.

3. the ARX model parameter obtained is extracted for concrete diagnostic sample, use Fuzzy C-Means Clustering (fuzzyC-means, be called for short FCM cluster) method according to similar failure mode, there is similar features, and inhomogeneity failure mode has the feature of dissimilar feature, realize the division of the set be made up of different faults pattern by its feature.FCM cluster is applicable to fuzzy data classification, can carry out actv. identification to the failure mode of system.

4. based on the trouble diagnosing of ARX model and FCM cluster, diagnostic procedure does not need complicated signal processing, do not need to run system model accurately, do not need to set up complicated diagnostic reasoning process yet, in diagnostic procedure, consumption calculations machine resource is little and be applicable to computer aided diagnosis, diagnostic mode is simply efficient, is easy to realize.

Accompanying drawing explanation

Fig. 1 is the steering hydraulic control system block diagram of traditional self-propelled hydraulic bogie.

Fig. 2 is the train schematic layout pattern of traditional self-propelled hydraulic bogie.

Fig. 3 is the structured flowchart of the fault localization system based on ARX model and FCM cluster of the present invention.

Fig. 3 A is the diagram of circuit of local diagnosis process of the present invention.

Fig. 4 is the structure diagram of circuit of the knowledge base based on ARX model and FCM cluster of the present invention.

Detailed description of the invention

Below in conjunction with accompanying drawing, the present invention is described in further detail.

A kind of fault localization system for self-propelled hydraulic bogie hydrostatic steering system of the present invention, this fault localization system is based on ARX model and FCM cluster, adopt C Plus Plus programming to obtain, this fault localization system is stored in each controller (as shown in Figure 2) of hydrostatic steering system.Described ARX model is active autoregressive model, auto-regressivewithextrainputsmodel, is called for short ARX model.Described FCM cluster is Fuzzy C-Means Clustering, fuzzyC-means, is called for short FCM cluster.In the present invention, quote that the definition of ARX and FCM model please refer to He Xiangyu, " the excavator hydraulic system trouble diagnosing based on active autoregressive model and Fuzzy C-Means Clustering " that what Tsing-Hua University delivers, Jilin University's journal (engineering version), 2008,38 (1): 13-187.They be different for the present invention to the quantity of information needed for the structure of ARX model and FCM cluster analysis, therefore the technical scheme of process is just different.

Shown in Fig. 3, Fig. 3 A, processing module 2 that fault localization system of the present invention includes sensor threshold value monitoring modular 1, threshold value transfinites, single-wheel tracking error monitoring modular 3, error transfinite processing module 4 and local diagnostic process module 5.

Sensor threshold value monitoring modular 1

In the present invention, the actual measurement sensory information of each train include the direction information Y of the single-wheel that angular-motion transducer gathers _{turn to}, pressure sensor export load-sensitive pump discharge pressure P _pressure, the temperature T of hydraulic oil that exports of temperature sensor _{oil temperature}.For actual measurement sensory information employing set expression-form be cL represents the identification number of train, and each train comprises 2 wheels.

Sensor threshold value monitoring modular 1 first aspect is for receiving each train actual measurement sensory information second aspect according to the priority of time of reception by described in each be stored in the data buffer of each controller of hydrostatic steering system; The third aspect adopts slip mean filter method to described carry out filtering process, obtain sensor monitoring value fourth aspect is by sensor monitoring value export to threshold value to transfinite processing module 2.

Threshold value transfinites processing module 2

In the present invention, sensor threshold value arranges W _{threshold value}what include angular-motion transducer turns to threshold information Y _in, pressure sensor export load-sensitive pump discharge threshold pressure P _in, the threshold temperature T of hydraulic oil that exports of temperature sensor _in, adopt set expression-form to be W _{threshold value}={ Y _in, P _in, T _in.For sensor monitoring value in any one amount be more than or equal to W _{threshold value}={ Y _in, P _in, T _inin corresponding amount, be then designated as and transfinite.Only have and be all less than sensor threshold value W is set _{threshold value}for not transfiniting.

Threshold value transfinites processing module 2 first aspect for receiving each train sensor monitoring value second aspect adopts out-of-limit condition to sensor monitoring value carry out judgement of transfiniting, obtain the result RS of threshold monitor ^cL; The third aspect will threshold monitor result exports to local diagnosis processing module 5; Fourth aspect will threshold monitor result exports to single-wheel tracking error monitoring modular 3.

In the present invention, RS is worked as ^cLwhen value is 1, represent that turning to loop that sensor threshold value occurs transfinites, and needs to carry out local diagnosis process; Work as RS ^cLwhen value is 0, represent that turning to loop that sensor threshold value does not occur transfinites, need to carry out single-wheel error sensing information acquisition process.

Single-wheel tracking error monitoring modular 3

Single-wheel tracking error monitoring modular 3 first aspect is according to the RS received ^cL=0(namely ) the direction information Y of single-wheel that gathers of threshold monitor result and angular-motion transducer _{turn to}carrying out transfinites compares, and obtains single-wheel and turns to the tracking error E that transfinites ^cLwhat=R-Y, R represented wheel turns to input, and Y represents the response output of wheel; Second aspect according to wheel previous moment t-1 turn to the tracking error that transfinites calculate and turn to the variable quantity that transfinites under current time t the third aspect will export to error to transfinite processing module 4.

Error transfinites processing module 4

Error processing module 4 first aspect that transfinites turns to the variable quantity that transfinites for receiving under current time t second aspect is according to turning to loop out-of-limit condition single-wheel tracking error is monitored, obtains the single-wheel monitoring result under current time export; Wherein, δ > 0, δ is detection sensitivity threshold value; The third aspect will the single-wheel result that do not transfinite exports to controller; Fourth aspect will the single-wheel result that transfinites exports to local diagnosis processing module 5.

Local diagnosis processing module 5

Local diagnosis processing module 5 first aspect is for receiving RS ^cLthe threshold monitor result of=1; Second aspect is used for receiving single-wheel to transfinite result; Third aspect foundation ARX model is respectively to RS ^cLthe threshold monitor result of=1 and the single-wheel result that transfinites process, obtain fault signature; Fourth aspect processes fault signature according to FCM cluster, obtains failure mode.

In the present invention, machinery the 06th phase in 2011 total 38th volume " maintenance with improve " has been contrasted in the division for failure mode corresponding to fault signature, " construction machinery and equipment steering swivel system failure analysis " that the people such as Wang Yuke deliver literary composition.Describe in literary composition about contents such as the phenomenon of the failure turned to, fault causes.From driving and the angle of service personnel, direct feel to steering swivel system fault signature mainly contain: hard steering, turn to steady, turning velocity is slow, the large hourly velocity of cornering resistance is slow.Table 1 gives the typical fault phenomenon of steering swivel system and the source of trouble thereof and failure mode analysis (FMA).

From detection complexity and the impact on system performance, above-mentioned fault is divided into dominant symbols and hidden failure.Dominant symbols refers to the fault type can seeing relevant alarm in electric-control system, and as oil filter blocking, pump outlet pressure is not enough.And hidden failure often can't see the alarm message of individual equipment or index.As hydraulic actuating cylinder leaks often because Long-Time Service causes piston or casing wall wearing and tearing, belong to element function and to degenerate the fault caused, conventional sensor threshold value monitoring and simple inference are difficult to realization and diagnose.When there is hidden failures, vehicle still can normally run, and performance can decline to some extent and bring potential safety hazard.Now, system still meets basic physics law, but performance degradation can change model parameter, utilizes the data fusion of object priori and multisensor to design intelligent diagnostics and prediction algorithm, is best approach concerning hidden failures.

Table 1 large engineering vehicle steering swivel system analyses of Common failure

In the present invention, shown in Fig. 3 A, controller, before carrying out local diagnosis process, first will gather IN (t)={ U, P according to the related content of table 1 _a, P _b, Q _a, Q _b, v, F} and OUT (t)=D _ap _a-D _bp _binformation builds ARX model, then adopts FCM clustering method to analyze, finally obtains sectionized matrix G=(g _ab) _{c × N}.

Build ARX model

Shown in Figure 4, in order to realize the trouble diagnosing to hydrostatic steering system, the quantity of information that building described ARX model needs includes apportioning valve spool control voltage U, hydraulic actuating cylinder rodless cavity pressure P _a, hydraulic actuating cylinder rod chamber pressure P _b, hydraulic actuating cylinder rodless cavity flow Q _a, hydraulic actuating cylinder rod chamber flow Q _b, hydraulic cylinder piston movement speed v, load force F on hydraulic actuating cylinder.According to the definition to ARX model in bibliography, the present invention then has steering swivel system ARX mode input variable in a sampling period to be IN (t)={ U, P _a, P _b, Q _a, Q _b, v, F}, output variable is OUT (t)=D _ap _a-D _bp _b, D _arepresent the effective active area of hydraulic actuating cylinder rodless cavity, D _arepresent the effective active area of hydraulic actuating cylinder rod chamber.

In the present invention, build quantity of information that described ARX model needs mainly from the angle of hydraulic pressure bogie production process mechanical equipment and operator safety, with reference in GB/T28264-2012 " elevator machinery security monitoring management system " to the definition of parameter relating to special vehicle production safety.Such monitoring scheme is conducive to improving reliability under hydraulic pressure bogie working environment and safety, guarantees field man safety.

FCM cluster

In the present invention, the analysis of FCM cluster is:

Steps A: note steering swivel system failure mode adds up to N(and a=1,2 ..., i ..., N), then by ARX mode input variable IN (t) corresponding for all for steering swivel system failure modes={ U, P _a, P _b, Q _a, Q _b, v, F} merging obtains steering swivel system autoregressive coefficient set X=[x ₁x ₂x _ix _n] ^tin;

Wherein, x ₁represent the steering swivel system autoregressive coefficient that the first failure mode is corresponding, x ₂represent the steering swivel system autoregressive coefficient that the second failure mode is corresponding, x _irepresent the steering swivel system autoregressive coefficient that i-th kind of failure mode is corresponding, also referred to as any steering swivel system autoregressive coefficient once, x _nrepresent the steering swivel system autoregressive coefficient that N kind failure mode is corresponding, be also the steering swivel system autoregressive coefficient that last a kind of failure mode is corresponding, T is the expression of matrix inversion.

Step B: the set that N number of failure mode forms is divided into C fault signature class (i.e. b=1,2 ..., C) and the sectionized matrix that obtains is designated as G=(g _ab) _{c × N}; And to autoregressive coefficient set X=[x ₁x ₂x _ix _n] ^tcarry out the initialization of sectionized matrix G;

Wherein, sectionized matrix G=(g _ab) _{c × N}line number in middle a representing matrix element is also a kind failure mode, the row number in b representing matrix element, is also the b kind fault signature in feature class C, g _abrepresent that a kind failure mode belongs to the degree of membership of b kind fault signature.

In the present invention, FCM clustering method is iterative algorithm, for convenience of description iterative process, and note kth step iteration gained sectionized matrix is designated as sectionized matrix during k=0 $G^{\left(0\right)}={\left(g_{\mathrm{ab}}^0\right)}_{C\×N}$ For iteration initial value, and $g_{\mathrm{ab}}^0=\frac{1}{C}.$

Step C: the center calculating C fault signature class

Step D: application regeneration block matrix obtain current sectionized matrix $G^{(k+1)}={\left(g_{\mathrm{ab}}^{k+1}\right)}_{C\×N},$ And $g_{\mathrm{ab}}^{(k+1)}=1/\underset{m=1}{\overset{C}{\mathrm{\Σ}}}{\left(\frac{\left|\right|x_i-c_j\left|\right|}{\left|\right|x_i-c_m\left|\right|}\right)}^2;$ K is iterative steps, iteration from k=0; x _irepresent the steering swivel system autoregressive coefficient that i-th kind of failure mode is corresponding, c _jrepresent C fault signature Leij center, c _mrepresent the m center of C fault signature class.

Step e: return step C and continue iteration, until || G ^(k+1)-G ^(k)||≤ε terminates, and ε represents the permissible error of iterative process.

In the present invention, after iteration completes, the sectionized matrix G after next time upgrading ^(k+1)be the sectionized matrix of this FCM cluster analysis, each element of sectionized matrix describes the degree of membership that corresponding steering swivel system ARX model parameter belongs to a certain state of the system.

Check step e classification results, if maximum membership degree value corresponding to same fault pattern be not at same subregion, then FCM cluster analysis failure is described.Classification results adjusts C feature class and iterative process permissible error ε, and re-executes step C, step D, until can correctly distinguish all failure modes.

In the present invention, at sectionized matrix G=(g _ab) _{c × N}in find out diagnostic sample, find out simultaneously belong to each faulty condition be subordinate to angle value g _ab.When the faulty condition corresponding to the maximum membership degree found out is the failure mode corresponding to diagnostic sample.

In the present invention, local diagnosis method in steering swivel system loop is divided into fault signature to extract and Fault Pattern Recognition two steps: what fault signature extracted employing is ARX seasonal effect in time series analysis method, by state of the system being agglomerated in ARX model parameter the linear fit of respective sensor data; FCM cluster analysis realizes the set of N+1 data point composition to be divided into C feature class by cluster iterative process, and by determining that the classification at diagnostic sample place is to realize trouble diagnosing.Steering swivel system loop local diagnosis method based on ARX model and FCM cluster does not need complicated signal processing, do not need to run system model accurately, also do not need to set up complicated diagnostic reasoning process, diagnostic procedure consumption calculations machine resource is little and be applicable to computer aided diagnosis.

Claims (5)

1. the fault localization system for self-propelled hydraulic bogie hydrostatic steering system, this fault localization system is stored in each controller of hydrostatic steering system, it is characterized in that: processing module (2) that this fault localization system includes sensor threshold value monitoring modular (1), threshold value transfinites, single-wheel tracking error monitoring modular (3), error transfinite processing module (4) and local diagnostic process module (5);

Sensor threshold value monitoring modular (1) first aspect is for receiving each train actual measurement sensory information second aspect according to the priority of time of reception by described in each be stored in the data buffer of each controller of hydrostatic steering system; The third aspect adopts slip mean filter method to described carry out filtering process, obtain sensor monitoring value fourth aspect is by sensor monitoring value export to threshold value to transfinite processing module (2); The actual measurement sensory information of each train include the direction information Y of the single-wheel that angular-motion transducer gathers _{turn to}, pressure sensor export load-sensitive pump discharge pressure P _pressure, the temperature T of hydraulic oil that exports of temperature sensor _{oil temperature};

Threshold value transfinites processing module (2) first aspect for receiving each train sensor monitoring value second aspect adopts out-of-limit condition to sensor monitoring value carry out judgement of transfiniting, obtain the result RS of threshold monitor ^cL; The third aspect is by RS ^cL=1 threshold monitor result exports to local diagnosis processing module (5); Fourth aspect is by RS ^cL=0 threshold monitor result exports to single-wheel tracking error monitoring modular (3);

Single-wheel tracking error monitoring modular (3) first aspect is according to the threshold monitor result RS received ^cL=0 to the direction information Y occurring the train CL that sensor threshold value transfinites _{turn to}compare, obtain turning to the tracking error E that transfinites ^cLwhat=R-Y, R represented wheel turns to input, and Y represents the response output of wheel; Second aspect according to wheel previous moment t-1 turn to the tracking error that transfinites calculate and turn to the variable quantity that transfinites under current time t the third aspect will export to error to transfinite processing module (4);

Error processing module (4) first aspect that transfinites turns to the variable quantity that transfinites for receiving under current time t second aspect is according to turning to loop out-of-limit condition single-wheel tracking error is monitored, obtains the single-wheel monitoring result under current time export; Wherein, δ > 0, δ is detection sensitivity threshold value; The third aspect will the single-wheel result that do not transfinite exports to controller; Fourth aspect will the single-wheel result that transfinites exports to local diagnosis processing module (5);

Local diagnosis processing module (5) first aspect is for receiving RS ^cLthe threshold monitor result of=1; Second aspect is used for receiving single-wheel to transfinite result; Third aspect foundation ARX model is respectively to RS ^cLthe threshold monitor result of=1 and the single-wheel result that transfinites process, obtain fault signature; Fourth aspect processes fault signature according to FCM cluster, obtains failure mode.

2. the fault localization system for self-propelled hydraulic bogie hydrostatic steering system according to claim 1, is characterized in that: sensor threshold value arranges W _{threshold value}what include angular-motion transducer turns to threshold information Y _in, pressure sensor export load-sensitive pump discharge threshold pressure P _in, the threshold temperature T of hydraulic oil that exports of temperature sensor _in, adopt set expression-form to be W _{threshold value}={ Y _in, P _in, T _in; For sensor monitoring value in any one amount be more than or equal to W _{threshold value}={ Y _in, P _in, T _inin corresponding amount, be then designated as and transfinite; Only have and be all less than sensor threshold value W is set _{threshold value}for not transfiniting.

3. the fault localization system for self-propelled hydraulic bogie hydrostatic steering system according to claim 1, is characterized in that: the structure of ARX model is that steering swivel system ARX mode input variable is IN (t)={ U, P in a sampling period _a, P _b, Q _a, Q _b, v, F}, output variable is OUT (t)=D _ap _a-D _bp _b, D _arepresent the effective active area of hydraulic actuating cylinder rodless cavity, D _arepresent the effective active area of hydraulic actuating cylinder rod chamber, U represents apportioning valve spool control voltage, P _arepresent hydraulic actuating cylinder rodless cavity pressure, P _brepresent hydraulic actuating cylinder rod chamber pressure, Q _arepresent hydraulic actuating cylinder rodless cavity flow, Q _brepresent hydraulic actuating cylinder rod chamber flow, v represents hydraulic cylinder piston moving velocity, F represents load force on hydraulic actuating cylinder.

4. the fault localization system for self-propelled hydraulic bogie hydrostatic steering system according to claim 1, is characterized in that: FCM cluster analysis has the following step:

Steps A: note steering swivel system failure mode adds up to N, and a=1,2 ..., i ..., N, then by ARX mode input variable IN (t) corresponding for all for steering swivel system failure modes={ U, P _a, P _b, Q _a, Q _b, v, F} merging obtains steering swivel system autoregressive coefficient set X=[x ₁x ₂x _ix _n] ^tin;

Wherein, x ₁represent the steering swivel system autoregressive coefficient that the first failure mode is corresponding, x ₂represent the steering swivel system autoregressive coefficient that the second failure mode is corresponding, x _irepresent the steering swivel system autoregressive coefficient that i-th kind of failure mode is corresponding, also referred to as any steering swivel system autoregressive coefficient once, x _nrepresent the steering swivel system autoregressive coefficient that N kind failure mode is corresponding, be also the steering swivel system autoregressive coefficient that last a kind of failure mode is corresponding, T is the expression of matrix inversion;

Step B: the set that N number of failure mode forms is divided into the sectionized matrix that C fault signature class obtain and is designated as G=(g _ab) _{c × N}; And b=1,2 ..., C, and to autoregressive coefficient set X=[x ₁x ₂x _ix _n] ^tcarry out the initialization of sectionized matrix G;

Wherein, sectionized matrix G=(g _ab) _{c × N}line number in middle a representing matrix element is also a kind failure mode, the row number in b representing matrix element, is also the b kind fault signature in feature class C, g _abrepresent that a kind failure mode belongs to the degree of membership of b kind fault signature;

Note kth step iteration gained sectionized matrix is designated as sectionized matrix during k=0 $G^{\left(0\right)}={\left(g_{ab}^0\right)}_{C\×N}$ For iteration initial value, and $g_{ab}^0=\frac{1}{C};$

Step C: the center calculating C fault signature class

Step D: application regeneration block matrix obtain current sectionized matrix $G^{(k+1)}={\left(g_{ab}^{k+1}\right)}_{C\×N},$ And $g_{ab}^{(k+1)}=1/\underset{m=1}{\overset{C}{\Σ}}{\left(\frac{\left|\right|x_i-c_j\left|\right|}{\left|\right|x_i-c_m\left|\right|}\right)}^2;$ K is iterative steps, iteration from k=0; x _irepresent the steering swivel system autoregressive coefficient that i-th kind of failure mode is corresponding, c _jrepresent C fault signature Leij center, c _mrepresent the m center of C fault signature class;

Step e: return step C and continue iteration, until || G ^(k+1)-G ^(k)||≤ε terminates, and ε represents the permissible error of iterative process.

5. the fault localization system for self-propelled hydraulic bogie hydrostatic steering system according to claim 1, it is characterized in that: controller is before carrying out local diagnosis process, first IN (t)={ U, P to be gathered according to the related content of table 1 large engineering vehicle steering swivel system analyses of Common failure _a, P _b, Q _a, Q _b, v, F} and OUT (t)=D _ap _a-D _bp _binformation builds ARX model, then adopts FCM clustering method to analyze, finally obtains sectionized matrix G=(g _ab) _{c × N}.