Fault prediction and condition-based repair method of urban rail train bogie

Patent metadata

Field	Value
Publication number	US-9714885-B2
Application number	US-201414890167-A
Country	US
Kind code	B2
Filing date	Dec 5, 2014
Priority date	Aug 28, 2014
Publication date	Jul 25, 2017
Grant date	Jul 25, 2017

Abstract

The present invention provides a fault prediction and condition-based repair method of an urban rail train bogie. An optimum service life distribution model of a framework, a spring device, a connecting device, a wheel set and axle box, a driving mechanism, and a basic brake device of a bogie is determined by adopting a method based on survival analysis; a reliability characteristic function of each subsystem is obtained; then, a failure rate of each subsystem of the bogie is calculated by adopting a neural network model optimized by an evolutionary algorithm; and finally, proportional risk modelling is conducted by taking the failure rate and safe operation days of each subsystem of the bogie as concomitant variables; and on the basis of cost optimization, thresholds and control limits for condition-based repair of a bogie system are obtained.

First claim

The invention claimed is:

1. A fault prediction and condition-based repair method of an urban rail train bogie, wherein the rail train bogie comprises six subsystems, including a framework, a spring device, a connecting device, a wheel set and axle box, a driving mechanism, and a basic brake device, and the method sequentially comprising as follows:
Step S1 including performing a censored processing based on a collected history failure data, determining a distribution model of each subsystem of the bogie based on a survival analysis method, obtaining a reliability characteristic function of each subsystem of the rail train bogie, calculating a reliability of each subsystem, and determining a subsystem with a lowest reliability as a most fragile part in the bogie;
Step S2 including calculating a failure rate of each subsystem of the bogie by adopting a neural network model optimized by an evolutionary algorithm through a digital signal processor (DSP);
Step S3 including conducting a proportional risk modeling by taking safe operation days and the calculated failure rate of each subsystem of the bogie as concomitant variables, and obtaining thresholds and control limits for the condition-based repair of the bogie, wherein an upper control limit is a failure threshold, and during a running process, once a system status value is found to exceed the upper control limit, the bogie is in an unusable status at this time, a corrective maintenance or replacement of one or more parts shall be performed before the bogie is reused, said parts comprising: a framework, a spring device, a connecting device, a wheel set and axle box, a driving mechanism, or a basic break device; a lower control limit is a preventive maintenance or replacement threshold, and indicates that a potential failure of the bogie starts to appear, and once a system status value exceeds the lower control limit, a corresponding troubleshooting or preventive maintenance shall be performed on a corresponding part, and if the system status value is lower than the lower control limit, the system does not need to be repaired;
wherein Step S1 further comprises steps as follows:
S11 including creating a two-parameter Weibull distribution model of the wheel set and axle box, the spring device, and the connecting device,

wherein a Failure Distribution Function is:

F
⁡

(
t
)

=

1
-

exp
⁢

{

-

[

t
η

]

β

}

a Reliability Function is:

R
⁡

(
t
)

=

exp
⁢

{

-

(

t
η

)

β

}

a Probability Density Function is:

f
⁡

(
t
)

=

β
η

⁢

(

t
η

)

β
-
1

⁢

exp
⁡

[

-

(

t
η

)

β

]

a Failure Rate Function is:

λ
⁡

(
t
)

=

β
η

⁢

(

t
η

)

β
-
1

wherein, t≧0, β>0, and η>0 , t is a time between failures, β and η are respectively a shape parameter and a scale parameter of the distribution;
S12 including creating a three-parameter Weibull distribution model of the framework, the driving mechanism, and the basic brake device,

wherein the Failure Distribution Function is:

F
⁡

(
t
)

=

1
-

exp
⁢

{

-

[

t
-
γ

η

]

β

}

the Reliability Function is:

R
⁡

(
t
)

=

exp
⁢

{

Assignees

• Univ Beijing Jiaotong

Inventors

• Qin Yong
• Jia Limin
• Shi Jingxuan
• Cheng Xiaoqing
• Zhang Yuan
• Yu Shan
• Kou Linlin
• Zhang Zhenyu

Classifications

• G01M17/10 Primary

  Suspensions, axles or wheels · CPC title

• G06N3/02

  Neural networks · CPC title

• G01M17/08 Primary

  Railway vehicles · CPC title

• G06N3/086

  using evolutionary algorithms, e.g. genetic algorithms or genetic programming · CPC title

• G06N3/006

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title