CN106406295A - Rail transit vehicle door system fault diagnosis and early warning method based on multiple conditions - Google Patents
Rail transit vehicle door system fault diagnosis and early warning method based on multiple conditions Download PDFInfo
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- CN106406295A CN106406295A CN201611094444.1A CN201611094444A CN106406295A CN 106406295 A CN106406295 A CN 106406295A CN 201611094444 A CN201611094444 A CN 201611094444A CN 106406295 A CN106406295 A CN 106406295A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
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Abstract
The invention discloses a rail transit vehicle door system fault diagnosis and early warning method based on multiple conditions. Operation data aiming at a door system is divided into initialized standard data, daily standard data and real-time operation data. Diagnosis and early warning can be accurately realized. Motor monitoring is used to monitor a parameter and a door controller IO signal in real time so as to diagnose a door-system typical fault, and a fault type of the door system, which can be diagnosed, is enriched. Through refining a fault characteristic, diagnosis precision is increased. A real-time health degree model is established, through a real-time parameter and a residual error characteristic of a model, a sub-health phenomenon is diagnosed, and a new method is provided for early warning of an early-stage fault. For a long-term slow degeneration sub-health problem of the door system, by using a traditional diagnosis method, a door system parameter can not be monitored; but, through using the method of the invention, by establishing a degeneration threshold model and comparing and analyzing a long-term change trend of a state parameter characteristic value, a long-term degeneration sub-health state can be identified, early stage early warning is realized and a good application prospect is possessed.
Description
Technical field
The present invention relates to urban rail transit technology field is and in particular to a kind of rail traffic vehicles door based on multi-state
System fault diagnosis and method for early warning.
Background technology
With the increasingly increasing of city size, urban track traffic carries huge traffic passenger flow pressure, for city
The monitor in real time of rail traffic vehicles state and reliability are it was also proposed that higher and higher requirement.
During urban rail transit vehicles operation, door system needs often to open and close, and adds anthropic factor
Impact, can lead to Train door Frequent Troubles, and proteges of the powerful who stay with their benefactions like parasites's event often occurs.Therefore, the reliability of door system and safety, will be straight
Connect the operation affecting urban rail transit vehicles normal safe.
The diagnosis logic of existing door system is relatively simple, and door system runs and later stage operation, peak period in the early stage
Run with storehouse, during operation, different operating modes are analyzed to the state parameter of door system and diagnosis has more artificial or system interference, no
Method meets the positioning of fault and the demand of failure cause investigation.Additionally, current door system adopts planned maintenance, during frame overhaul by
In lacking diagnostic means, whole updating is carried out to critical component, maintenance cost has been significantly greatly increased.Therefore, hand over for city rail
The intelligent comprehensive diagnosis of the door system being open to traffic and the research of method for early warning, for the job stability of urban rail transit vehicles
Lifting is significant.
At present, the diagnosis of existing door system and operation maintenance, mainly has two ways：One is the regular inspection of attendant
Look into maintenance, waste time and energy, poor reliability；Two is according to reliability analysis technology tracing trouble type and reason after fault occurs,
Including reliability block diagram method, Bayesian network method, Fault Tree etc., but this kind of diagnostic techniquess need to rely in a large number sufficiently expert
Based on knowledge base, there is no real-time, be not suitable for feature mining and initial failure early warning yet.
Research with the status safety monitoring aspect of door system is constantly advancing and perfect, the door system of big data quantity
Real time data can be acquired, and wherein comprises abundant door system status information.So, for the door of urban rail transit vehicles
System diagnoses and method for early warning it is necessary to invent a kind of multi-state data with online real time execution for the resultant fault driving,
To realize urban rail transit vehicles door from time-based checking maintenance pattern to the checking maintenance mould based on state and risk
Formula changes, and is current urgent problem.
Content of the invention
The invention aims to overcoming the problems of existing rail traffic vehicles door system.The base of the present invention
In rail traffic vehicles door system fault diagnosis and the method for early warning of multi-state, can be to the typical case of rail traffic vehicles door system
Fault type carries out accurately identifying, door system subhealth state and degradation trend being carried out early warning, improves rail traffic vehicles door
The reliability of system operation, has a good application prospect.
In order to achieve the above object, the technical solution adopted in the present invention is：
Rail traffic vehicles door system fault diagnosis based on multi-state and method for early warning it is characterised in that：Including following
Step,
Step (A), gathers each car door by the sensor in rail traffic vehicles door system motor and gating device
Service data, and store in data base；
Described service data includes corner, rotating speed, electric current, torque, temperature, I/O signal and switch triggering critical CAP value；
Step (B), the service data of storage is carried out pretreatment, is classified, specific as follows：
Newly reached the standard grade and adjusted the normal, service data of the rail traffic vehicles door system of empty wagons running-in to be defined as initially
Change normal data A, for the original state as rail traffic vehicles door system, and the ginseng as long-term degradation subhealth state parameter
Examine benchmark；
Before daily rail traffic vehicles are reached the standard grade, multiple switching door normal data when checking in factory storehouse is defined as daily
Normal data B, for the normalcy daily as rail traffic vehicles door system；
After daily rail traffic vehicles are reached the standard grade, the sensor of rail traffic vehicles door system and gating device Real-time Collection are each
The service data of car door is defined as real time data C, as Real-Time Switch gate-shaped in the middle of operation for the rail traffic vehicles door system
State, discriminates whether that current door system breaks down or real-time subhealth state is abnormal；
The simulation test data that rail traffic vehicles are carried out in test-bed, for auxiliary formulate typical fault rule and
Subhealth state rule；
Step (C), the rail traffic vehicles door system after daily rail traffic vehicles are reached the standard grade carries out Representative Faults Diagnosis,
Real time data C is carried out with population characteristic value extraction, judges whether real time data C belongs to failure classes abnormal, if preliminary judgement result
It is yes, then adopts typical fault decision rule to judge fault type, be concurrently out of order warning and fault parameter；If preliminary judgement is tied
Fruit is no, then enter step (D)；
Step (D), the rail traffic vehicles door system after daily rail traffic vehicles are reached the standard grade carries out real-time subhealth state shape
State diagnoses, and real time data C is analyzed with daily normal data B, builds feature based according to daily normal data B and divides
The data statisticss model of cloth parameter as real time health degree model, by calculating the kinematic feature factor of real time data C with real time
The residual error ratio of health degree model relatively, switch critical triggering CAP and be compared with switch standard amount of over-pressurization scope, setting health degree threshold
The real-time subhealth state that value realizes rail traffic vehicles door system quantifies, and judges whether fiducial value exceeds health degree threshold value, if exceeding
Health degree threshold value, according to beyond the points of health degree threshold value and the subhealth state exception class of location determination rail traffic vehicles door system
Type, and continue Frequency and the front-rear switch door state of this subhealth state Exception Type of monitoring and statisticses, finally combine expertise
Storehouse diagnosis obtains the current sub-health state of rail traffic vehicles door system, and according to this sub-health state, carries out initial failure
Early warning；If without departing from health degree threshold value, enter step (E)；
Step (E), carries out the subhealth state diagnosis of long-term degradation type, to daily criterion numeral to rail traffic vehicles door system
According to B and initialization normal data A be analyzed, with initialize normal data A typical characteristics as reference standard, set up
Degradation model, by analyzing the Secular Variation Tendency of daily normal data B critical eigenvalue, pre- by least square method
Survey the critical eigenvalue in next cycle, if the critical eigenvalue of prediction exceeds the scope of degradation model, output track is handed over
Logical train-door system degeneration subhealth state early warning；Otherwise, rail traffic vehicles door system normally runs.
The aforesaid rail traffic vehicles door system fault diagnosis based on multi-state and method for early warning it is characterised in that：Step
Suddenly (C) carries out population characteristic value extraction to real time data C, judges whether real time data C belongs to failure classes abnormal, if preliminary judgement
Result is yes, then adopt typical fault decision rule to judge fault type, and be concurrently out of order warning and fault parameter, specifically includes
Following steps,
(C1), real time data C alignd, remove the pretreatment that redundancy gathers, and the source of an allusion is distinguished by global feature value
Type fault data；
(C2), extract typical fault data corresponding refinement global feature value, including opening-closing door time, total kilometres, maximum
Stroke, maximum current, effective current, motor stall number of times, motor rotation blockage number of times, motor stall position, motor rotation blockage position
Put, open the door direction times of exercise and direction times of exercise of closing the door；
(C3), refined global feature value and typical fault rule are compared, the cloth model being divided according to critical eigenvalue
Enclose the corresponding fault type determining current failure data, described critical eigenvalue is opening-closing door time, total kilometres；
(C4), the corresponding fault type of current failure data will be determined, by the failure warning of rail traffic vehicles door system
It is pushed to live customer service and related maintenance personnel with fault parameter information through mail, the real-time online realizing door system fault is examined
Disconnected.
The aforesaid rail traffic vehicles door system fault diagnosis based on multi-state and method for early warning it is characterised in that：Step
Suddenly (D), the data statisticss model of feature based distributed constant is built as real time health degree model according to daily normal data B,
Specifically include following steps,
(D1) daily normal data B alignd, remove the pretreatment that redundancy gathers；Reality is distinguished by global feature value
When subhealth state data；
(D2) according to motion feature respectively by corner, rotating speed and current data be divided into startup section, raising speed section, at the uniform velocity section,
Braking section and jogging section, and extract each section time, maximum, minima, average, standard deviation, the degree of bias, kurtosis feature；
(D3) the switch critical triggering CAP value tag of switch gate process is gathered by gating device；
(D4) in order to weigh the intensity of variation of real time data eigenvalue, corner rotating speed, current parameters, with same door system
Daily normal data B and its segmentation feature value, as training sample, construct real time health degree model, if training sample database X, such as public
Shown in formula (1),
Wherein, { [xi1,xi2,...,xim], i=1,2 ..., n } it is by the segmentation feature value of i-th group of data, corner sequential
The characteristic quantity that data, rotating speed time series data and electric current time series data are constituted, n represents total sample group number, the number of m representative feature
Amount；
(D5) according to training sample database X, calculate the average statistical of each characteristic quantity of its population sampleAnd standard deviationAs public affairs
Shown in formula (2) and formula (3),
(D6) combine 3sigma criterion, build the envelope scope of real time health degree model：WithAs early warning envelope
Boundary,As grave warning envelope boundary, obtain real time health degree model.
The aforesaid rail traffic vehicles door system fault diagnosis based on multi-state and method for early warning it is characterised in that：Step
Suddenly (D), switch the scope of critical triggering CAP between 6-23；Described switch standard amount of over-pressurization scope is between 1.5mm-6mm.
The aforesaid rail traffic vehicles door system fault diagnosis based on multi-state and method for early warning it is characterised in that：Step
Suddenly (D), the value switching critical triggering CAP is 18；Described switch standard amount of over-pressurization is 4mm.
The aforesaid rail traffic vehicles door system fault diagnosis based on multi-state and method for early warning it is characterised in that：Step
Suddenly (E), with initialize normal data A typical characteristics as reference standard, set up degradation model, specifically include following step
Suddenly,
(E1) typical characteristics in initialization normal data A with significant change trend are filtered out；
(E2) make all typical characteristics Z be training sample database, obtain degradation model in conjunction with 3sigma criteria construction,
WithAs degeneration subhealth state threshold value of warning,As fault pre-alarming threshold value, wherein,Total for training sample database Z
The average statistical of each characteristic quantity of body sample；Standard deviation for each characteristic quantity of population sample of training sample database Z.
The invention has the beneficial effects as follows：The rail traffic vehicles door system fault diagnosis based on multi-state of the present invention and pre-
Alarm method, the service data for door system is divided into initialization normal data, daily normal data and real-time running data, has
Effect decrease the impact to lasting door system data analysiss of anthropic factor and system degradation, can more accurately realize diagnosis and
Early warning, monitors real-time monitoring parameter and gating device I/O signal diagnosis door system typical fault using motor, enriching door system can
Tracing trouble type, and by refining fault signature, improve diagnostic accuracy；Set up real time health degree model, joined by real-time
Number diagnoses Sub-health with the residual error feature of model, is that initial failure early warning provides new method；Door system is delayed for a long time
The slow inferior health problem degenerated, traditional diagnosis method substantially cannot monitoring door systematic parameter, and this method is by setting up degeneration threshold
Value model, the Secular Variation Tendency of relative analyses state parameter eigenvalue, it is capable of identify that long-term degradation sub-health state, and realize
Early warning, has a good application prospect.
Brief description
Fig. 1 is the present invention based on the rail traffic vehicles door system fault diagnosis of multi-state and the flow process of method for early warning
Figure.
Fig. 2 is the flow chart of the Representative Faults Diagnosis of the present invention.
Fig. 3 is the flow chart of the real-time sub-health state diagnosis of the present invention.
Fig. 4 is the flow chart of the subhealth state diagnosis of the long-term degradation type of the present invention.
Specific embodiment
Below in conjunction with Figure of description, the present invention is further illustrated.
The rail traffic vehicles door system fault diagnosis based on multi-state of the present invention and method for early warning, for door system
Service data is divided into initialization normal data, daily normal data and real-time running data, effectively reduce anthropic factor and
The impact to lasting door system data analysiss for the system degradation, can more accurately realize diagnosis and early warning, using motor monitoring
Real-time monitoring parameter and gating device I/O signal diagnosis door system typical fault, enrich the diagnosable fault type of door system, and logical
Cross refinement fault signature, improve diagnostic accuracy；Set up real time health degree model, by the residual error feature of real-time parameter and model
Diagnosis Sub-health, is that initial failure early warning provides new method；The inferior health problem that door system is slowly degenerated for a long time,
Traditional diagnosis method substantially cannot monitoring door systematic parameter, and this method is by setting up degradation model, relative analyses state
The Secular Variation Tendency of parameter attribute value, is capable of identify that long-term degradation sub-health state, and realizes early warning, as shown in figure 1,
Comprise the following steps,
Step (A), gathers each car door by the sensor in rail traffic vehicles door system motor and gating device
Service data, and store in data base；
Described service data includes corner, rotating speed, electric current, torque, temperature, I/O signal and switch triggering critical CAP value；
Step (B), the service data of storage is carried out pretreatment, is classified, specific as follows：
Newly reached the standard grade and adjusted the normal, service data of the rail traffic vehicles door system of empty wagons running-in to be defined as initially
Change normal data A, for the original state as rail traffic vehicles door system, and the ginseng as long-term degradation subhealth state parameter
Examine benchmark；
Before daily rail traffic vehicles are reached the standard grade, multiple switching door normal data when checking in factory storehouse is defined as daily
Normal data B, for the normalcy daily as rail traffic vehicles door system；
After daily rail traffic vehicles are reached the standard grade, the sensor of rail traffic vehicles door system and gating device Real-time Collection are each
The service data of car door is defined as real time data C, as Real-Time Switch gate-shaped in the middle of operation for the rail traffic vehicles door system
State, discriminates whether that current door system breaks down or real-time subhealth state is abnormal；
The simulation test data that rail traffic vehicles are carried out in test-bed, for auxiliary formulate typical fault rule and
Subhealth state rule；
Here initialization normal data A, daily normal data B, at least more than 30 groups virtual values of real time data C；
Step (C), the rail traffic vehicles door system after daily rail traffic vehicles are reached the standard grade carries out Representative Faults Diagnosis,
Process, as shown in Fig. 2 real time data C is carried out with population characteristic value extraction, judges whether real time data C belongs to failure classes extremely,
If preliminary judgement result is yes, fault type is judged using typical fault decision rule, be concurrently out of order warning and fault ginseng
Number, specifically includes following steps,
(C1), real time data C alignd, remove the pretreatment that redundancy gathers, and the source of an allusion is distinguished by global feature value
Type fault data；
(C2), extract typical fault data corresponding refinement global feature value, including opening-closing door time, total kilometres, maximum
Stroke, maximum current, effective current, motor stall number of times, motor rotation blockage number of times, motor stall position, motor rotation blockage position
Put, open the door direction times of exercise and direction times of exercise of closing the door；
(C3), refined global feature value and typical fault rule are compared, the cloth model being divided according to critical eigenvalue
Enclose the corresponding fault type determining current failure data, described critical eigenvalue is opening-closing door time, total kilometres；
(C4), the corresponding fault type of current failure data will be determined, by the failure warning of rail traffic vehicles door system
It is pushed to live customer service and related maintenance personnel with fault parameter information through mail, the real-time online realizing door system fault is examined
Disconnected；
If preliminary judgement result is no, enter step (D)；
Step (D), the rail traffic vehicles door system after daily rail traffic vehicles are reached the standard grade carries out real-time subhealth state shape
State diagnose, process as shown in figure 3, being analyzed with daily normal data B to real time data C, according to daily normal data B
The data statisticss model building feature based distributed constant as real time health degree model, by calculating the motion of real time data C
The residual error ratio of characteristic parameter and real time health degree model relatively, switch critical triggering CAP and compared with switch standard amount of over-pressurization scope
Relatively, set the real-time subhealth state quantization that health degree threshold value realizes rail traffic vehicles door system, judge fiducial value whether beyond strong
Kang Du threshold value, if exceeding health degree threshold value, according to beyond the points of health degree threshold value and location determination rail traffic vehicles door system
The subhealth state Exception Type of system, and continue Frequency and the front-rear switch door state of this subhealth state Exception Type of monitoring and statisticses,
Final diagnosis with reference to expert knowledge library obtains the current sub-health state of rail traffic vehicles door system, and according to this subhealth state shape
State, carries out initial failure early warning；If without departing from health degree threshold value, enter step (E)；
The data statisticss model of feature based distributed constant is built as real time health degree mould according to daily normal data B
Type, specifically includes following steps,
(D1) daily normal data B alignd, remove the pretreatment that redundancy gathers；Reality is distinguished by global feature value
When subhealth state data；
(D2) according to motion feature respectively by corner, rotating speed and current data be divided into startup section, raising speed section, at the uniform velocity section,
Braking section and jogging section, and extract each section time, maximum, minima, average, standard deviation, the degree of bias, kurtosis feature；
(D3) the switch critical triggering CAP value tag of switch gate process is gathered by gating device；
(D4) in order to weigh the intensity of variation of real time data eigenvalue, corner rotating speed, current parameters, with same door system
Daily normal data B and its segmentation feature value, as training sample, construct real time health degree model, if training sample database X, such as public
Shown in formula (1),
Wherein, { [xi1,xi2,...,xim], i=1,2 ..., n } it is by the segmentation feature value of i-th group of data, corner sequential
The characteristic quantity that data, rotating speed time series data and electric current time series data are constituted, n represents total sample group number, the number of m representative feature
Amount；
(D5) according to training sample database X, calculate the average statistical of each characteristic quantity of its population sampleAnd standard deviationAs public affairs
Shown in formula (2) and formula (3),
(D6) combine 3sigma criterion, build the envelope scope of real time health degree model：WithAs early warning envelope
Boundary,As grave warning envelope boundary, obtain real time health degree model.
The scope of the critical triggering CAP of described switch between 6-23, preferable 18, and can be adjusted according to practical situation
Whole；Described switch standard amount of over-pressurization scope between 1.5mm-6mm, preferable 4mm, and being adjusted according to practical situation；
Directly it is judged to that amount of over-pressurization is abnormal beyond critical range, one it is found that be judged to door system subhealth state immediately, and be given corresponding etc.
Level Forewarning Measures；After the completion of the real-time inferior health problem of door system is processed, the threshold value of warning to door system is needed to reset, will in real time
Subhealth state model relearns data after adjustment；
Step (E), carries out the subhealth state diagnosis of long-term degradation type, process such as Fig. 4 institute to rail traffic vehicles door system
Show, daily normal data B and initialization normal data A is analyzed, to initialize the typical characteristics of normal data A
For reference standard, set up degradation model, by analyzing the Secular Variation Tendency of daily normal data B critical eigenvalue, lead to
Cross the critical eigenvalue that least square method predicts the next cycle, if the critical eigenvalue of prediction exceeds the model of degradation model
Enclose, then output track vehicular traffic door system degeneration subhealth state early warning；Otherwise, rail traffic vehicles door system normally runs, with
The typical characteristics of initialization normal data A are reference standard, set up degradation model, specifically include following steps,
(E1) typical characteristics in initialization normal data A with significant change trend are filtered out；
(E2) make all typical characteristics Z be training sample database, obtain degradation model in conjunction with 3sigma criteria construction,
WithAs degeneration subhealth state threshold value of warning,As fault pre-alarming threshold value, wherein,For training sample database Z
The average statistical of each characteristic quantity of population sample；Standard deviation for each characteristic quantity of population sample of training sample database Z.
Above-mentioned 3sigma criterion is modeled according to mean value ± 3/6* standard deviation, and 3sigma criterion is this area skill
The art personnel conventional meanses used in modeling.
Because each door system on-line time does not have periodically, the therefore data volume according to daily normal data B and pre-
In the survey cycle, handling averagely at equal intervals is carried out to daily normal data B eigenvalue, afterwards all kinds of eigenvalue variation tendencies is carried out
Least square fitting, and compared with door system degradation model threshold value according to fitted trend predictive value, moved back according to the door system of prediction
Change severity and provide corresponding early warning, by mail reminder scene customer service and related maintenance personnel's timely inspection door system mode, and
Early replacing degeneration parts.
In sum, the rail traffic vehicles door system fault diagnosis based on multi-state of the present invention and method for early warning, energy
Enough typical fault types to rail traffic vehicles door system carry out accurately identifying, door system subhealth state and degradation trend are carried out
Early warning, improves rail traffic vehicles door system reliability of operation, has a good application prospect.
Ultimate principle and principal character and the advantages of the present invention of the present invention have been shown and described above.The technology of the industry
, it should be appreciated that the present invention is not restricted to the described embodiments, the simply explanation described in above-described embodiment and description is originally for personnel
The principle of invention, without departing from the spirit and scope of the present invention, the present invention also has various changes and modifications, these changes
Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and its
Equivalent thereof.
Claims (6)
1. the rail traffic vehicles door system fault diagnosis based on multi-state and method for early warning it is characterised in that：Walk including following
Suddenly,
Step (A), gathers the operation of each car door by the sensor in rail traffic vehicles door system motor and gating device
Data, and store in data base；
Described service data includes corner, rotating speed, electric current, torque, temperature, I/O signal and switch triggering critical CAP value；
Step (B), the service data of storage is carried out pretreatment, is classified, specific as follows：
Newly reached the standard grade and adjusted the normal, service data of the rail traffic vehicles door system of empty wagons running-in and be defined as initialization mark
Quasi- data A, for the original state as rail traffic vehicles door system, and the reference base as long-term degradation subhealth state parameter
Accurate；
Before daily rail traffic vehicles are reached the standard grade, multiple switching door normal data when checking in factory storehouse is defined as daily standard
Data B, for the normalcy daily as rail traffic vehicles door system；
After daily rail traffic vehicles are reached the standard grade, the sensor of rail traffic vehicles door system and each car door of gating device Real-time Collection
Service data be defined as real time data C, as rail traffic vehicles door system operation in the middle of Real-Time Switch door state, sentence
Not not whether current door system breaks down or real-time subhealth state is abnormal；
The simulation test data that rail traffic vehicles are carried out in test-bed, formulates typical fault rule for auxiliary and Asia is good for
Health rule；
Step (C), the rail traffic vehicles door system after daily rail traffic vehicles are reached the standard grade carries out Representative Faults Diagnosis, to reality
When data C carry out population characteristic value extraction, judge whether real time data C belongs to failure classes abnormal, if preliminary judgement result is yes,
Typical fault decision rule is then adopted to judge fault type, be concurrently out of order warning and fault parameter；If preliminary judgement result is
No, then enter step (D)；
Step (D), the rail traffic vehicles door system after daily rail traffic vehicles are reached the standard grade carries out real-time sub-health state and examines
Disconnected, real time data C is analyzed with daily normal data B, feature based distribution ginseng is built according to daily normal data B
The data statisticss model of number as real time health degree model, by calculating kinematic feature factor and the real time health of real time data C
The residual error ratio of degree model relatively, switch critical triggering CAP and be compared with switch standard amount of over-pressurization scope, set health degree threshold value in fact
The real-time subhealth state of existing rail traffic vehicles door system quantifies, and judges whether fiducial value exceeds health degree threshold value, if beyond health
Degree threshold value, according to beyond the points of health degree threshold value and the subhealth state Exception Type of location determination rail traffic vehicles door system,
And continue Frequency and the front-rear switch door state of this subhealth state Exception Type of monitoring and statisticses, finally examine with reference to expert knowledge library
Break and obtain the current sub-health state of rail traffic vehicles door system, and according to this sub-health state, carry out initial failure early warning；
If without departing from health degree threshold value, enter step (E)；
Step (E), carries out the subhealth state diagnosis of long-term degradation type, to daily normal data B to rail traffic vehicles door system
With initialization normal data A be analyzed, with initialize normal data A typical characteristics as reference standard, foundation is moved back
Change threshold model, by analyzing the Secular Variation Tendency of daily normal data B critical eigenvalue, predicted by least square method
The critical eigenvalue in next cycle, if the critical eigenvalue of prediction exceeds the scope of degradation model, output track traffic
Train-door system degeneration subhealth state early warning；Otherwise, rail traffic vehicles door system normally runs.
2. the rail traffic vehicles door system fault diagnosis based on multi-state according to claim 1 and method for early warning, its
It is characterised by：Step (C) carries out population characteristic value extraction to real time data C, judges whether real time data C belongs to failure classes different
Often, if preliminary judgement result is yes, fault type is judged using typical fault decision rule, be concurrently out of order warning and fault
Parameter, specifically includes following steps,
(C1), real time data C alignd, remove the pretreatment that redundancy gathers, and typical case's event is distinguished by global feature value
Barrier data；
(C2), extract typical fault data corresponding refinement global feature value, including opening-closing door time, total kilometres, maximum row
Journey, maximum current, effective current, motor stall number of times, motor rotation blockage number of times, motor stall position, motor rotation blockage position,
Enabling direction times of exercise and direction times of exercise of closing the door；
(C3), refined global feature value and typical fault rule are compared, true according to the cloth scope that critical eigenvalue divides
The corresponding fault type of settled prior fault data, described critical eigenvalue is opening-closing door time, total kilometres；
(C4) the corresponding fault type of current failure data will, be determined, by the failure warning of rail traffic vehicles door system and event
Barrier parameter information passes through mail push to live customer service and related maintenance personnel, realizes the real-time online diagnosis of door system fault.
3. the rail traffic vehicles door system fault diagnosis based on multi-state according to claim 1 and method for early warning, its
It is characterised by：Step (D), builds the data statisticss model of feature based distributed constant as real-time according to daily normal data B
Health degree model, specifically includes following steps,
(D1) daily normal data B alignd, remove the pretreatment that redundancy gathers；Real-time Asia is distinguished by global feature value
Health data；
(D2) corner, rotating speed and current data are divided into respectively by startup section, raising speed section, at the uniform velocity section, deceleration according to motion feature
Section and jogging section, and extract each section time, maximum, minima, average, standard deviation, the degree of bias, kurtosis feature；
(D3) the switch critical triggering CAP value tag of switch gate process is gathered by gating device；
(D4) in order to weigh the intensity of variation of real time data eigenvalue, corner rotating speed, current parameters, daily with same door system
Normal data B and its segmentation feature value, as training sample, construct real time health degree model, if training sample database X, such as formula
(1) shown in,
Wherein, { [xi1,xi2,...,xim], i=1,2 ..., n be by i-th group of data segmentation feature value, corner when ordinal number
The characteristic quantity constituting according to, rotating speed time series data and electric current time series data, n represents total sample group number, the quantity of m representative feature；
(D5) according to training sample database X, calculate the average statistical of each characteristic quantity of its population sampleAnd standard deviationAs formula
(2) and shown in formula (3),
(D6) combine 3sigma criterion, build the envelope scope of real time health degree model：WithAs early warning envelope boundary,As grave warning envelope boundary, obtain real time health degree model.
4. the rail traffic vehicles door system fault diagnosis based on multi-state according to claim 1 and method for early warning, its
It is characterised by：Step (D), the scope switching critical triggering CAP is between 6-23；Described switch standard amount of over-pressurization scope exists
1.5mm-6mm between.
5. the rail traffic vehicles door system fault diagnosis based on multi-state according to claim 4 and method for early warning, its
It is characterised by：Step (D), the value switching critical triggering CAP is 18；Described switch standard amount of over-pressurization is 4mm.
6. the rail traffic vehicles door system fault diagnosis based on multi-state according to claim 1 and method for early warning, its
It is characterised by：Step (E), with initialize normal data A typical characteristics as reference standard, set up degradation model, tool
Body comprises the following steps,
(E1) typical characteristics in initialization normal data A with significant change trend are filtered out；
(E2) make all typical characteristics Z be training sample database, obtain degradation model in conjunction with 3sigma criteria construction, withAs degeneration subhealth state threshold value of warning,As fault pre-alarming threshold value, wherein,Overall for training sample database Z
The average statistical of each characteristic quantity of sample；Standard deviation for each characteristic quantity of population sample of training sample database Z.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102829967A (en) * | 2012-08-27 | 2012-12-19 | 中国舰船研究设计中心 | Time-domain fault identifying method based on coefficient variation of regression model |
| CN103345207A (en) * | 2013-05-31 | 2013-10-09 | 北京泰乐德信息技术有限公司 | Mining analyzing and fault diagnosis system of rail transit monitoring data |
| US20140067327A1 (en) * | 2011-05-03 | 2014-03-06 | China Real-Time Technology Co., Ltd. | Similarity curve-based equipment fault early detection and operation optimization methodology and system |
| CN103699698A (en) * | 2014-01-16 | 2014-04-02 | 北京泰乐德信息技术有限公司 | Method and system for track traffic failure recognition based on improved Bayesian algorithm |
| CN104091070A (en) * | 2014-07-07 | 2014-10-08 | 北京泰乐德信息技术有限公司 | Rail transit fault diagnosis method and system based on time series analysis |
| CN105045256A (en) * | 2015-07-08 | 2015-11-11 | 北京泰乐德信息技术有限公司 | Rail traffic real-time fault diagnosis method and system based on data comparative analysis |
| US20160180610A1 (en) * | 2014-12-23 | 2016-06-23 | Palo Alto Research Center Incorporated | System And Method For Determining Vehicle Component Conditions |
-
2016
- 2016-12-02 CN CN201611094444.1A patent/CN106406295B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140067327A1 (en) * | 2011-05-03 | 2014-03-06 | China Real-Time Technology Co., Ltd. | Similarity curve-based equipment fault early detection and operation optimization methodology and system |
| CN102829967A (en) * | 2012-08-27 | 2012-12-19 | 中国舰船研究设计中心 | Time-domain fault identifying method based on coefficient variation of regression model |
| CN103345207A (en) * | 2013-05-31 | 2013-10-09 | 北京泰乐德信息技术有限公司 | Mining analyzing and fault diagnosis system of rail transit monitoring data |
| CN103699698A (en) * | 2014-01-16 | 2014-04-02 | 北京泰乐德信息技术有限公司 | Method and system for track traffic failure recognition based on improved Bayesian algorithm |
| CN104091070A (en) * | 2014-07-07 | 2014-10-08 | 北京泰乐德信息技术有限公司 | Rail transit fault diagnosis method and system based on time series analysis |
| US20160180610A1 (en) * | 2014-12-23 | 2016-06-23 | Palo Alto Research Center Incorporated | System And Method For Determining Vehicle Component Conditions |
| CN105045256A (en) * | 2015-07-08 | 2015-11-11 | 北京泰乐德信息技术有限公司 | Rail traffic real-time fault diagnosis method and system based on data comparative analysis |
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