Short Circuit Fault Detection against High Thermal Background Using a Two-Level Scheme Based on DoG Filter

Short circuit is a key factor which drastically affects the efficiency of metal electrorefining. Infrared image of the intercell busbar region is used to perform short circuit detection. To cope with the high thermal background, a two-level short circuit detection method is designed. Firstly, with background subtraction, high intensity short circuit electrodes, as well as the background, are removed, and normal working electrodes are preserved. In the second stage, suspicious short circuit areas are sifted out by normal electrode detecting and texture period estimation. Gaussian difference filter (DoG) which is based on the human visual system is improved to match the target gray distribution. A comparative experiment indicates that the proposed orthogonal DoG outperforms the original DoG and top-hat in the accuracy of normal electrode detection. The two-level detection method in this paper is applied in a copper electrolysis plant and exhibits superiority in locating short circuits and avoiding miss detection.

On one approach to the detection of infrasonic signals of irregular natural phenomena in the instrumental observations time series at the student interdisciplinary testing ground

The work is devoted to the analysis of time series and the problem of processing signals obtained as a result of the design approach implementation during the organization of instrumental observations of irregular natural phenomena at the student interdisciplinary testing ground. The objective of the work is to study the methods of processing noisy signals obtained as a result of monitoring the infrasonic environment, which make it possible to automate the search for fragments of the time series generated by irregular natural phenomena. At the beginning of the work, a brief explanation of the essence of the measuring scientific experiment carried out within the framework of the project approach used in the additional education of students and schoolchildren shall be given. The following is a review of publications describing various approaches to the analysis of nonstationary time series obtained in the process of instrumental observations. As the main method of time series analysis, it is proposed to use the algorithm for calculating the fractal dimension of the time series, proposed by T. Higuchi [1]. During studying of the time series of infrasonic signals, a number of regularities were discovered that contribute to the development of an original procedure for processing and transforming the signal under study, which makes it possible to determine the time intervals of fragments of the time series corresponding to the signals of the desired natural phenomena. The essence of the proposed approach lies in the preliminary preparation of the time series by processing the data with a simple normalized difference filter, previously smoothed by performing the coenvolution (convolution) operation with a Gaussian kernel; determining the step of segmenting the normalized time series, calculating fractal dimensions and averaged amplitudes for each of the segments of the time series and obtaining on their basis vectors of changes in dimensions and amplitudes with their subsequent element-wise multiplication. It is shown that the maximum values of the components of the resulting vector are indicators of timestamps for the location of the desired signals.

Orbit Determination Using Pulsar Timing Data and Orientation Vector

X-ray Pulsar Navigation (XPNAV) uses the Time Difference of Arrival (TDOA) of the pulsar signal between the spacecraft and Solar System Barycentre (SSB) to determine position. In this paper, a novel method to improve the performance of XPNAV via exploiting the pulsar position vector is proposed. First, the field of view of the collimator is utilised to find the pulsar orientation direction. Then, a searching strategy based on the modified Powell method under given coordinate frames is proposed. We also mathematically prove the existence of the extreme value of the searching strategy. Subsequently, an observation model based on the pulsar radiation vector is presented and applied to formulate the observation function together with pulsar time transfer function. Finally, an Adaptive Divided Difference Filter (ADDF) algorithm is introduced to iteratively estimate the position and velocity of the spacecraft. Numerical simulations show that the vector searching method is feasible and the pulsar radiation direction can improve the navigation performance by 75%. The simulation results also show that the ADDF performs better than Unscented Kalman Filtering (UKF) and DDF in position estimation.

A DDF-based IMM-TFS Approach for the Accuracy Evaluation Problem of Rapid Transfer Alignment

This study aims to address the accuracy evaluation problem for rapid transfer alignment with the coexistence of large misalignment angles and uncertain observation noises. For the requirement of accuracy evaluation, complete information in terms of misalignment angles should be estimated during the alignment process. Thus, a fixed-interval smoothing approach is the core of solving this problem. In this paper, a new Divided Difference Filter (DDF)-based an Interacting Multiple Model Two-Filter Smoother (IMM-TFS) is developed to estimate the misalignment angles. The proposed DDF-based IMM-TFS releases the restriction of inverse nonlinearity by using the weighted statistical linearization regression method, and the resulting pseudo-linear model can be used for backward-time IMM filtering. The smoothing step takes into account the merging of estimations and the interaction of multiple models simultaneously. The new smoother is compared with the previous well-known methodologies in simulations. The results show that the DDF-based IMM-TFS can achieve better accuracy for misalignment angles estimation, and has a high efficiency for detecting the changes in a model.