Method for monitoring service state of switch rail based on feature fusion

A method for monitoring a service state of a switch rail based on feature fusion, comprising:

• S1:
• mounting an ultrasonic guided wave monitoring apparatus on the switch rail to perform online defect identification and simulation of the switch rail and establishing a baseline signal library, wherein the ultrasonic guided wave monitoring apparatus generates a guided wave signal propagating along the switch rail and receives an echo signal of the guided wave signal:
• S1.0:
• acquiring, by the ultrasonic guided wave monitoring apparatus, an echo signal x′(n) in real time, wherein the echo signal is acquired by sampling multiple sample points continuously in a time series;
• n denotes a serial number of the sample point in the echo signal;
• the echo signal comprises a healthy echo signal x′1(n) of the switch rail in a healthy state and a defective echo signal x′2(n) of the switch rail in a simulated defective state;
• and the healthy echo signal x′1(n) and the defective echo signal x′2(n) have an identical count;
• S1.1:
• preprocessing the acquired echo signal x′ (n) by normalization:
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• wherein M denotes a length of the echo signal x′(n), wherein M is a total count of sample points in the echo signal;
• and n denotes the serial number of the sample point in the echo signal;
• through the preprocessing, a healthy baseline signal x1(n) and a defective baseline signal x2(n) are acquired;
• the healthy baseline signal x1(n) and the defective baseline signal x2(n) are baseline signals x(n);
• all different baseline signals totaling BN form the baseline signal library;
• and the healthy baseline signal x1(n) and the defective baseline signal x2(n) each account for half in the baseline signal library;
• S2:
• performing feature extraction on the preprocessed baseline signal x(n);
• S3:
• randomly extracting a healthy baseline signal of the switch rail in the healthy state from the baseline signal library and processing the healthy baseline signal to acquire a feature vector as a healthy feature vector Fbaseline:
• Fbaseline=(SF1baseline to S18phaseline,ESEF1baseline,ESEF2baseline)
• wherein SF1baseline to SF18baseline denote first 18 feature parameters of the healthy feature vector Fbaseline, respectively, and ESEF1baseline, ESEF2baseline denote last two feature parameters of the healthy feature vector Fbaseline, respectively;
• nondimensionalizing each baseline signal x(n) in the baseline signal library:
• e(Fmonitoring/Fbaseline)−e
• wherein e denotes a Napierian base;
• acquiring, by the nondimensionalization, a feature vector ssf of the guided wave signal of the switch rail:
• ssf=(SF1dimensionless to SF18dimensionless,ESEF1dimensionless,ESEF2dimensionless)
• wherein SF1dimensionless to SF18dimensionless denote first 18 feature parameters of the feature vector ssf of the guided wave signal of the switch rail, respectively, and ESEF1dimensionless, ESEF2dimensionless denote last two feature parameters of the feature vector ssf of the guided wave signal of the switch rail, respectively;
• S4:
• selecting a defect-sensitive feature from the feature vector ssf of the guided wave signal of the switch rail and acquiring, by a binary particle swarm optimization (BPSO) algorithm, a best feature subset by:
• calculating a fitness function of a population after each iteration, updating a global best solution gbest, and taking a final global best solution gbest as the best feature subset SSFgbest, SSFgbest {ssf1,ssf2, . . . , ssfBN}, BN denoting the count of the baseline signals in the baseline signal library;
• S5:
• acquiring an automatic online defect identification model of the switch rail based on a least squares support vector machine (LS-SVM):
• taking the best feature subset SSFgbest as a training set of the LS-SVM and training by a cross-validation method to acquire the automatic online defect identification model of the switch rail based on the LS-SVM;
• and
• S6:
• monitoring the switch rail by the automatic online defect identification model of the switch rail based on the LS-SVM:
• processing, by steps S1 to S3, a target echo signal MS(n) of the switch rail acquired in real time to acquire a feature vector of a guided wave signal of the switch rail using ssfms=(SF1dimensionless to SF18dimensionless, ESEF1dimensionless, ESEF2dimensionless);
• performing, by step S4, feature selection on the feature vector ssfms of the guided wave signal of the switch rail based on a serial number NF of a feature to acquire a feature vector ssfmsnew of the guided wave signal of the switch rail;
• inputting the feature vector SSfmsnew of the guided wave signal of the switch rail into the automatic online defect identification model of the switch rail based on the LS-SVM acquired in step S5;
• and outputting a current service state of the switch rail, wherein the current service state of the switch rail is a healthy state or a defective state.