Research On Signal Process And Quantitative Recognition Method Of Broken Wires In Wire Rope

The wire rope is widely used in engineering as a key load-bearing component.During a long-term service, by the effect of enviromental corrosion, uncertainalternating load, mechanical impact and wear etc., damages such as wire breaking,wear, rust will happen, which cumulatively decreases the strength or even lead tofracture of the wire rope. Consequently it may result in a standstill of the machine or afatal crash. Therefore, the detection and accurate identification of the damagecondition of the wire rope in order to avoid the wire breaking accident is of socialsignificance and huge ecomonical benefit.Because of the intricate structure of the wire ropes, the complicated relationbetween the diverse damages and the variation and feature of the magnetic fieldleading to the damages, and multidisciplinary fields involved, by far there is nocomplet theory and effective method formed on the detection of the wire rope damage.Moreover, the instruments developed are not univerisal and usually giveinreproducible and inaccurate results.In this thesis, the most commn damage, wire breaking, is studied theoretically andexperimentally by the design of exciter and detector, signal collectionn and processing,feature extraction and quantitative recognition,etc..Firstly, by analyzing the mode of magnetization and magnetic field characteristicsof wire rope, the analysis and calculation method of excitation circuit, optimumselecting principle of magnetic materials and the law of the exciter structureinfluencing on magnetic field are studied and a excitation device is designed. Thedetection principle of leakage magnetic field based on the hall element, the detectordesign, the method of signal preprocessing and acquisition are investigated. A test rigfor the detection of wire breaking is setted up and the theoretical analysis and designare verified through the experiments.Secondly, the signal denoising method by improving the threshold based on thecorrelation of multi-dimension wavelet coefficients is studied. According to thecharacteristics that the density and the amplitude of noise are decreased with the increase of the layer of wavelet decomposition, but wire breaking signal is opposite,the concept of characteristic coefficient has been put forward. The characteristiccoefficient can characterize the nature of signal, which of wire breaking signal is small,but the one of noise signal is big, the characteristic coefficient is adopted to decreasethe threshold in the wire breaking signal, while increase the threshold in the noise.With the processing of wavelet decomposition coefficients based on the improvedthreshold, the adaptive change of threshold point by point can be realized, so thepurpose of better retaining wire breaking signal and removing noise signal is reached.At the same time, the noise reduction method is studied which chooses the bestpredictor and updater point by point based on the autocorrelation coefficientcharacterizing the local characteristics of wire breaking signal. This method achievedthe effective noise reduction for signal detection on the premise of remaining usefulbroken wire signal. The superiority of the above methods’s noise reduction effect isverified through the experiment of denoising processing of simulating signal and theactual detection signal.Thirdly, the time domain and time-frequency domain characteristics of thedetection signal and the dipartite degree of wire breaking are analyzed. The divisionalperformance of signal power spectral entropy and centroid frequency to wire breakingposition, fracture width and the number of broken wires is studied through theoreticalanalysis and experiment. The results show that the two-dimensional information madeup of power spectral entropy and centroid frequency can effectively distinguish theposition of broken wires, but has a poor differentiation to the fracture width and thenumber of broken wires. Based on the study of the multi-domain characteristics ofdetection signal when the wire rope appears wire breaking, the quantitativeidentification method for broken wire is furtherly put forward which takes the mixedfeature vector consisting of the frequency domain characteristics (power spectralentropy and centroid frequency), time-frequency domain characteristic(wavelet energy)and time domain features (peak value, area of waveform and wave width) as input. Theseparability criterion and experiment prove that using mixed feature vector for brokenwire identification has better separability than using time domain and time-frequency domain feature vector. Therefore, the mixed feature vector can be used as the effectivecharacteristics input of the quantitative identification when wire rope appears brokenwires.At last, the basis and method of building quantitative identification models ofBP neural network, RBF neural network and support vector machine (SVM) for brokenwire are studied. Taking the mixed feature vector as input, the BP neural networkmodel based on pattern matching, the RBF neural network model based on the functionapproximation theory and the support vector machine model are respectivelyestablished and trained by the same data. Then the performance of the three trainedmodels is compared using the same test sample sets to test the model. Results showthat BP neural network model can be used for damage identification of wire rope, RBFneural network model is slightly better than BP neural network, support vectormachine model has superior generalization performance and identification effect underthe condition of small sample of broken wire. SVM provides an effective way forsolving the problem of broken wire’s quantitative identification under the condition ofsmall sample.On the basis of theoretical analysis and experimental research on the BP neuralnetwork model, RBF neural network model and support vector machine model forbroken wire’s quantitative identification, the multi-model fusion decision-makingrecognition method is proposed for the broken wire based on Dempster-Shaferevidential theory. The theory basis and method are researched including the building ofmulti-model fusion recognition model, the construction of frame of discernment,determination of basic probability assignment function of evidence and establishmentof evidential combination algorithm and decision rule. Then a multi-model fusiondecision-making recognition system for broken wire based on D-S evidence theory isconstructed and the experiments are carried on. The results show that the accuracy andthe reliability of quantitative recognition results for broken wire with multi-modelfusion decision-making recognition system, compared with the single modelidentification, are significantly improved. It is a new effective method for brokenwire’s quantitative identification.