A Fast Signal Estimation Method Based on Probability Density Functions for Fault Feature Extraction of Rolling Bearings

Fault diagnosis of rolling bearings is essential to ensure the efficient and safe operation of mechanical equipment. The extraction of fault features of the repetitive transient component from noisy vibration signals is key to bearing fault diagnosis. However, the bearing fault-induced transients are often submerged by strong background noise and interference. To effectively detect such fault-related transient components, this paper proposes a probability- and statistics-based method. The maximum-a-posteriori (MAP) estimator combined with probability density functions (pdfs) of the repetitive transient component, which is modeled by a mixture of two Laplace pdfs and noise, were used to derive the fast estimation model of the transient component. Subsequently, the LapGauss pdf was adopted to model the noisy coefficients. The parameters of the model derived could then be estimated quickly using the iterative expectation–maximization (EM) algorithm. The main contributions of the proposed statistic-based method are that: (1) transients and their wavelet coefficients are modeled as mixed Laplace pdfs; (2) LapGauss pdf is used to model noisy signals and their wavelet coefficients, facilitating the computation of the proposed method; and (3) computational complexity changes linearly with the size of the dataset and thus contributing to the fast estimation, indicated by analysis of the computational performance of the proposed method. The simulation and experimental vibration signals of faulty bearings were applied to test the effectiveness of the proposed method for fast fault feature extraction. Comparisons of computational complexity between the proposed method and other transient extraction methods were also conducted, showing that the computational complexity of the proposed method is proportional to the size of the dataset, leading to a high computational efficiency.

Targeting of RBM10 to S1-1 Nuclear Bodies: Targeting Sequences and its Biological Significance

RBM10 is an RNA-binding protein that regulates alternative splicing (AS). This protein localizes to the extra-nucleolar nucleoplasm and S1-1 nuclear bodies (NBs). We investigated the biological significance of RBM10 localization to S1-1 NBs, which is poorly understood. Our analyses revealed that RBM10 possesses two S1-1 NB-targeting sequences (NBTSs), one in the KEKE motif region and another in the C2H2 Zn finger (ZnF). These NBTSs acted synergistically and were sufficient for localization of RBM10 to S1-1 NBs. Furthermore, the C2H2 ZnF not only acted as an NBTS, but was also essential for regulation of AS by RBM10. RBM10 did not participate in S1-1 NB formation. We confirmed the previous finding that localization of RBM10 to S1-1 NBs increases as cellular transcriptional activity decreases and vice versa. These results indicate that RBM10 is a transient component of S1-1 NBs and is sequestered in these structures via its NBTSs when cellular transcription decreases. We propose that the NB-targeting activity of the C2H2 ZnF is induced when it is not bound to pre-mRNA or the splicing machinery complex under conditions of reduced transcription.

Short-term Changes in the Mean: 3. Permanent Versus Transient Response

In a variety of settings—additive epistasis in a diploid, dominance in an autotetraploids, shared environmental effects (such as epigenetic contributions), maternal effects, and dominance under inbreeding—the response in the mean has both a permanent and a transient component. The latter arises because selection perturbs the population distribution of genotypes away from their Hardy-Weinberg values. Upon the cessation of selection, any change in allele frequencies remains, but any additional changes due to departures from Hardy-Weinberg decay away. The result is that, even in the presence of these transient components, the breeder's equation often accurately predicts the amount of permanent response.

Evolution of the low-frequency pulse profile of PSR B2217+47

An evolution of the low-frequency pulse profile of PSR B2217+47 is observed during a six-year observing campaign with the LOFAR telescope at 150 MHz. The evolution is manifested as a new component in the profile trailing the main peak. The leading part of the profile, including a newly-observed weak component, is steady during the campaign. The transient component is not visible in simultaneous observations at 1500 MHz using the Lovell telescope, implying a chromatic effect. A variation in the dispersion measure of the source is detected in the same timespan. Precession of the pulsar and changes in the magnetosphere are investigated to explain the profile evolution. However, the listed properties favour a model based on turbulence in the interstellar medium (ISM). This interpretation is confirmed by a strong correlation between the intensity of the transient component and main peak in single pulses. Since PSR B2217+47 is the fourth brightest pulsar visible to LOFAR, we speculate that ISM-induced pulse profile evolution might be relatively common but subtle and that SKA-Low will detect many similar examples. In this scenario, similar studies of pulse profile evolution could be used in parallel with scintillation arcs to characterize the properties of the ISM.

Multichannel Sleep Spindle Detection using Sparse Low-Rank Optimization

BackgroundWe propose a multichannel spindle detection method that detects global and local spindle activity across all channels of scalp EEG in a single runNew MethodUsing a non-linear signal model, which assumes the multichannel EEG to be a sum of a transient component and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated multichannel oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindlesResults and comparison with other methodsSeveral examples are shown to illustrate the utility of the proposed method in detecting global and local spindle activity. The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F1scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4 minutes to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parametersConclusionsThe proposed method aims to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen