Patent ID: 9714885
Date: 2017-07-25
CPC Classifications: G01M,G06N

Claim:
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: a Reliability Function is: a Probability Density Function is: a Failure Rate Function is: 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: the Reliability Function is: the Probability Density Function is: the Failure Rate Function is: wherein t is a time between failures, β>0 and η>0, β and η are respectively a shape parameter and a scale parameter of the distribution, γ is a location parameter of the distribution; wherein Step S2 further comprises steps as follows: creating a back propagation (BP) neural network model based on Particle Swarm Optimization (PSO), optimizing a BP neural network connecting weight by adopting PSO, training the BP neural network, and during modeling, determining PSO parameters, and setting the parameters as follows: S21 selecting a particle number as 30; S22 selecting a maximum particle velocity as 0.5; S23 updating a speed and a location of a particle provided by an acceleration sensor based on equations as follows: wherein, in the above equations, v S24 using a fitness function as follows, S25 initializing; normalizing input and output data to [−1,1], wherein particle swarms generated after initializing represent a neural network combined by different weights and thresholds, and generate an initial BP network structure; S26 evaluating; calculating a fitness value based on the equation in S24; S27 updating a location and a velocity; continuously updating locations of particles by means of comparing the fitness values, taking an individual extremum with the best fitness value as a global extremum, taking the corresponding weight and threshold as a current optimum solution of the particle swarm, and updating the velocity; S28 determining termination of the algorithm; terminating iteration when it reaches maximum iteration times or the fitness value reaches an expected error; S29 survival of the optimum solution; when the iteration is terminated, a weight and a threshold corresponding to a global weight is an optimum solution for a training sample; and S30 performing learning by substituting the optimum solution into the BP neural network model, for anticipating a failure rate; obtaining a failure rate threshold h * : wherein, t is a time between failures, β>0 , and η>0, β and η are respectively a shape parameter and a scale parameter of the distribution, γ is a location parameter of the distribution, and X(t) is a concomitant variable; and S32 making a draft by taking a working time t as an x-coordinate, and taking a value of  as a y-coordinate, wherein an upper line in the drawing indicates the upper control limit corresponding to the failure rate threshold, and a lower line indicates the lower control limit corresponding to the failure rate threshold.