A new method of vibration signal denoising based on improved wavelet

Noise cancellation is one of the most successful applications of the wavelet transform. Its basic idea is to compare wavelet decomposition coefficients with the given thresholds and only keep those bigger ones and set those smaller ones to zero and then do wavelet reconstruction with those new coefficients. It is most likely for this method to treat some useful weak components as noise and eliminate them. Based on the cyclostationary property of vibration signals of rotating machines, a novel wavelet noise cancellation method is proposed. A numerical signal and an experimental signal of rubbing fault are used to test and compare the performances of the new method and the conventional wavelet based denoising method provided by MATLAB. The results show that the new noise cancellation method can efficaciously suppress the noise component at all frequency bands and has better denoising performance than the conventional one.

Quantifying dating uncertainties in layer-counted paleoclimate proxy archives

Paleoclimate proxy records have non-negligible uncertainties that arise from both the proxy measurement and the dating processes. Knowledge of the dating uncertainties is important for a rigorous propagation to further analyses; for example for identification and dating of stadial-interstadial transitions in Greenland ice core records during glacial intervals, for comparing the variability in different proxy archives, and for model-data comparisons in general. In this study we develop a statistical framework to quantify and propagate dating uncertainties in layer-counted proxy archives using the example of the Greenland Ice Core Chronology 2005 (GICC05). We express the number of layers per depth interval as the sum of a structured component that represents both underlying physical processes and biases in layer counting, described by a regression model, and a noise component that represents the fluctuations of the underlying physical processes, as well as unbiased counting errors. The age-depth relationship of the joint dating uncertainties can then be described by a multivariate Gaussian process from which realizations of the chronology can be sampled. We show how the effect of an unknown counting bias can be incorporated in our framework and present refined estimates of the occurrence times of Dansgaard-Oeschger events evidenced in Greenland ice cores together with a complete uncertainty quantification of these timings.

Математический алгоритм для обнаружения утечек малой интенсивности

The operating principle of leak detection systems, based on registration of transported medium hydroacoustic fluctuations, appearing due to pipeline loss of containment, consists of identification of hydraulic impulse, originating in case of leakage, using acoustic dynamic pressure measuring sensors - hydrophones. However, during pumping at pipeline stationary operating mode hydrophones also register background noises, which can mask the leakage signal. To separate the useful leakage signal it is important to construct an algorithm that allows lowering the noise component of the signals. Within the scope of experimental research, two pairs of hydrophones were used, which were installed at the functioning main oil pipeline at a distance of 20 km of each other. The distance between the adjacent paired hydrophones was no more than 1 km. Leaks were imitated by draining the product (diesel fuel) in the middle of control section. Authors considered the methods of noisy signals filtration and possible methods of cleared signals processing to determine the leak parameters. Mathematical algorithm that allows minimizing the influence of signal noise by filtration and mutual hydrophone readings compensation was proposed. It is established, that the developed algorithm allows detecting the leakages of low intensity (up to 0.1 % of actual flow) in cases of stationary pipeline operating mode and pumping stop mode. Принцип работы систем обнаружения утечек, основанных на регистрации гидроакустических колебаний транспортируемой среды, возникающих из-за разгерметизации трубопровода, состоит в идентификации гидравлического импульса, возникающего при образовании утечки, с помощью акустических датчиков измерения динамического давления – гидрофонов. Однако гидрофоны в процессе перекачки при стационарном режиме работы трубопровода регистрируют в том числе фоновые шумы, которые могут маскировать сигнал от утечки. Для выделения полезного сигнала утечки актуально построение алгоритма, позволяющего понизить шумовые составляющие сигналов. В рамках экспериментальных исследований использовались две пары гидрофонов, которые устанавливались на действующем магистральном нефтепродуктопроводе на расстоянии 20 км друг от друга. Расстояние между соседними гидрофонами в паре составляло не более 1 км. Утечки имитировались путем выполнения натурных сливов продукта (дизельного топлива) в середине контрольного участка. Авторами рассмотрены методы фильтрации зашумленных сигналов и возможные способы обработки очищенных сигналов с целью определения параметров утечки. Предложен математический алгоритм, позволяющий минимизировать влияние шумовых составляющих сигналов путем фильтрации и взаимной компенсации показаний пар гидрофонов. Установлено, что разработанный алгоритм позволяет обнаруживать утечки малой интенсивности (до 0,1 % от фактического расхода) в условиях стационарного режима работы трубопровода и режима остановленной перекачки.

Timing and spectral analysis of 2S 1417−624 during its 2018 outburst

We investigate timing and spectral characteristics of the transient X-ray pulsar 2S 1417−624 during its 2018 outburst with NICER follow up observations. We describe the spectra with high-energy cut-off and partial covering fraction absorption (PCFA) model and present flux-dependent spectral changes of the source during the 2018 outburst. Utilizing the correlation-mode switching of the spectral model parameters, we confirm the previously reported sub-critical to critical regime transitions and we argue that secondary transition from the gas-dominated to the radiation pressure-dominated disc do not lead to significant spectral changes below 12 keV. Using the existing accretion theories, we model the spin frequency evolution of 2S 1417−624 and investigate the noise processes of a transient X-ray pulsar for the first time using both polynomial and luminosity-dependent models for the spin frequency evolution. For the first model, the power density spectrum of the torque fluctuations indicate that the source exhibits red noise component (Γ ∼ −2) within the timescales of outburst duration which is typical for disc-fed systems. On the other hand, the noise spectrum tends to be white on longer timescales with high timing noise level that indicates an ongoing accretion process in between outburst episodes. For the second model, most of the red noise component is eliminated and the noise spectrum is found to be consistent with a white noise structure observed in wind-fed systems.

The Application of Deep Learning Algorithms for PPG Signal Processing and Classification

Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach.

Highly Reliable Method to Obtain a Correlation Coefficient Unaffected by the SEM Noise Component for Examining the Degree of Specimen Damage due to Electron Beam Irradiation

A correlation coefficient is often used as a measure of the strength of a linear relationship (i.e., the degree of similarity) between two sets of data in a variety of fields. However, in the field of scanning electron microscopy (SEM), it is frequently difficult to properly use the correlation coefficient because SEM images generally include severe noise, which affects the measurement of this coefficient. The current study describes a method of obtaining a correlation coefficient that is unaffected by SEM noise in principle. This correlation coefficient is obtained from a total of four SEM images, comprising two sets of two images with identical views, by calculating several covariance values. Numerical experiments confirm that the measured correlation coefficients obtained using the proposed method for noisy images are equal to those for noise-free images. Furthermore, the present method can be combined with a highly accurate and noise-robust position alignment as needed. As one application, we show that it is possible to immediately examine the degree of specimen damage due to electron beam irradiation during a certain SEM observation, which has been difficult until now.

A Fuzzy Logic Approach for Image Restoration and Content Preserving

Image filtering, which removes or reduces noises from the contaminated images, is an important task in image processing. This paper presents a novel approach to the problem of noise reduction for gray-scale images. The proposed technique is able to remove the noise component, while adapting itself to the local noise intensity. In this way, the proposed algorithm can be considered as a modification of the median filter driven by fuzzy membership functions. Experimental results are compared to static median filter by numerical measures and visual inspection. As was expected, the new filter shows better performances.

An Intelligent reference free adaptive learning algorithm for speech enhancement

Removing of noise component is an important task in all practical applications like hearing aids, speech therapy etc. In speech communication applications the speech components are contaminated with various types of noises. Separation of speech and noise component is a key issue in hearing aids, speech therapy applications. This paper demonstrates a hybrid version of singular spectrum analysis (SSA) and independent component analysis (ICA) based adaptive noise canceller (ANC) to separate noise and speech components. As ICA is not suitable for single channel sources, SSA is used to map signal channel data to multivariant data. Therefore, SSA based ICA decomposition is used to generate reference for noise cancellation process. Variable Step based adaptive learning algorithm is used to separate noise contaminations from speech signals. To reduce computational complexity of system, sign regressor operation is applied to the data vector of the proposed adaptive learning methodology. Performance measures such as Signal to noise ratio improvement, excess mean square error and misadjustment are calculated for various considered ANCs, their values for crane noise are 29.6633 dB, – 27.4854 dB and 0.2058 respectively. Among the various adaptive learning algorithms, sign regressor based step variable method performs better than the other algorithms. Hence this learning methodology is well suited for hearing aids and speech therapy applications due to its robustness, less computational complexity and filtering ability.

A new method to identify rotor–stator rubbing positions based on intrinsic time scale decomposition

To effectively identify the rotor–stator rubbing positions in aero-engine, the paper has proposed the combination of intrinsic time-scale decomposition (ITD) and classification algorithm. Regarding that with larger noise component in proper rotation component (PRC) signals after ITD, it will be more difficult to extract the characteristic information of rubbing faults, the PRC correspondings to the largest noise was eliminated. Meanwhile, signals were reconstructed based on residual proper rotation components, and positions of rubbing faults were identified according to the reconstructed signal. As rubbing extent and other factors cannot be completely the same in each rubbing, energy of reconstructed signal has been normalized to reduce the difference. Normalized energy indexes were inputted into classification algorithm as feature vectors to identify the positions of rubbing faults. To identify the superiority of approach, a comparison has been made between the proposed approach and the method of directly extracting normalized energy indexes of acceleration signals. The result of comparison shows that the two methods both work well in the identification rate of training and test samples; as for the identification rate for an unknown sample, the proposed method is superior to the other, with identification rate increasing by 17% and 9.4%.