Wire Rope Weak Defect Signal Processing Methods Based on Improved SVD and Phase Space Reconstruction

Due to the complexity of the interference operation environment of wire rope, the detection signals are usually weak and coupled in time-frequency domain, which makes the defect difficult to recognize, while the signal characterizations in phase space are also needed to be studied. Combining the nonlinear dynamic feature identification theories, phase space characteristics and chaotic features of wire rope defect detection signals are mainly investigated in this paper. First, principles of phase space reconstruction method for wire rope detection signals are presented by the chaotic dynamic indexes calculation of embedded dimension and delay time. Second, the change trends of the correlation dimension, approximate entropy and Lyapunov index of different phase space reconstructed wire rope defect detection signals are studied through the nonlinear simulation and analysis. Finally, a phase space reconstruction algorithm based on improved SVD is proposed, and the new algorithm is also compared with traditional signal processing methods. These results obtained by 6 groups of experiments were also evaluated and compared by the parameters of signal-to-noise ratio (SNR) and phase space trajectory chart, which manifests that the improved algorithm not only can increase the weak detection signal SNR to about 2.3dB of wire rope effectively, but also demonstrate the feasibility of the proposed methods in application.

Mechanical Fault Diagnosis of High Voltage Circuit Breaker based on Improved GSO-SVM Algorithm

The vibration signal generated by the transmission and impact of mechanical components of circuit breaker has chaotic performances, which is difficult to be analysed by conventional signal processing methods. The phase space reconstruction of vibration signal is worked on, and the signal reconstruction parameters are calculated by mutual information method and Cao algorithm respectively. The vibration signal is reconstructed into a high-dimensional space, and its permutation entropy is calculated as a feature vector. Support vector machine (SVM) is used to identify the failure type of circuit breaker, and PSO improved GSA hybrid algorithm is used to optimize the parameters of SVM so as to obtain high recognition accuracy. The experiment is carried out with the measured vibration signal of the typical operation state of the circuit breaker. The results show that the characteristics of circuit breaker vibration signals can be extracted accurately with the combination of phase space reconstruction and permutation entropy. By using PSO-GSA-SVM, the fault types of circuit breakers can be identified quickly and effectively, and the problems of path distortion, energy leakage and mode overlap of existing diagnosis methods can be solved.

A Study On Methods for Determining Phase Space Reconstruction Parameters

Several pairs of algorithms were used to determine the phase space reconstruction parameters to analyze the dynamic characteristics of chaotic time series. The reconstructed phase trajectories were compared with the original phase trajectories of the Lorenz attractor, Rössler attractor, and Chens attractor to obtain the optimum method for determining the phase space reconstruction parameters with high precision and efficiency. The research results show that the false nearest neighbor method and the complex auto-correlation method provided the best results. The saturated embedding dimension method based on the saturated correlation dimension method is proposed to calculate the time delay. Different time delays are obtained by changing the embedding dimension parameters of the complex auto-correlation method. The optimum time delay occurs at the point where the time delay is stable. The validity of the method is verified through combing the application of correlation dimension, showing that the proposed method is suitable for the effective determination of the phase space reconstruction parameters.

DETECTION OF VENTRICULAR FIBRILLATION USING WAVELET TRANSFORM AND PHASE SPACE RECONSTRUCTION FROM ECG SIGNALS

This study proposes the detection of ventricular fibrillation (VF) by wavelet transforms (WTs) and phase space reconstruction (PSR) from electrocardiogram (ECG) signals. A neural network with weighted fuzzy memberships (NEWFM) is used to detect VF as a classifier. In the first step, the WT was used to remove noise in ECG signals. In the second step, coordinates were mapped from the wavelet coefficients by the PSR. In the final step, NEWFM used the mapped coordinates-based features as inputs. The NEWFM has the bounded sum of weighted fuzzy memberships (BSWFM) that can easily appear the distinctness between the normal sinus rhythm (NSR) and VF in the graphical characteristics. The BSWFM can easily be set up in a portable automatic external defibrillator (AED) to detect VF in an emergency.