Enhancing Detection Performance of the Phase-Sensitive OTDR Based Distributed Vibration Sensor Using Weighted Singular Value Decomposition

An accurate fault location method for wireless sensor network based on random matrix theory

In order to improve the effect of fault location, this paper proposes an accurate fault location method for wireless sensor networks based on random matrix theory. The standard non Hermite matrix is used to extract accurate fault location data. Considering the volatility of the original data, the original random matrix is preprocessed. Based on the real-time sliding time window method, the space-time characteristic data of network faults are determined, and the precise fault location of wireless sensor networks based on random matrix theory is realized.Experimental results show that the false positive rate of the proposed method is only 2%. The average fault location accuracy is as high as 96.4% and the fault location time is only 15.1 s, which shows that the proposed method has a good location effect.

White light interference demodulation of optical fiber Fabry-Perot micro-pressure sensors based on the Karhunen-Loeve transform and singular value decomposition

In this paper, the Karhunen-Loeve transform (KLT) and wavelength domain interferometric spectral singular value decomposition (SVD) are used for the first time to demodulate the pressure of an optical fiber Fabry-Perot (F-P) micro-pressure sensor, and the feasibility of the proposed method is demonstrated experimentally. The eigenvalue decomposition of the dominant frequency part of the beam-domain interferometric spectrum after the fast Fourier transform (FFT) is performed using KLT, and the singular value decomposition of the wavelength domain interferometric spectrum is additionally performed using SVD. Both methods use high-order eigenvalues as a new metric and then derive the relation between the new metric and the reference pressure. The two demodulation methods are experimentally compared, and we used an optical fiber F-P pressure sensor with unknown structure and material for pressure measurements. Even though the interferometric spectral signal is acquired using a coarse spectrometer (2.5 nm wavelength resolution), one can still achieve high demodulation accuracy with both algorithms. However, the SVD demodulation accuracy decreases significantly after reducing the spectral data points in the wavelength domain from 1566 to 783. KLT still has high demodulation accuracy and linearity after spectral data points are reduced from 1024 to 256 in the wavenumber domain. The satisfactory linearity of the measured pressure versus reference pressure and low reading errors validate the feasibility of the proposed demodulation algorithm.

Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.