A DPCA-based online fault indicator for gear faults using three-direction vibration signals

For online monitoring and identifying gear faults, a new fault indicator is proposed based on a multivariate statistical technique, dynamic principal component analysis (DPCA), under variable load conditions. In this method, a tri-axial vibration sensor is used to acquire the 3-direction vibration signals of gear in the gear box because it can pick up more abundant fault information than a single axis sensor does. By monitoring the value of the fault indicator, the running state of the gear (normal condition or faults) can be directly identified according to the set thresholds without using any other fault classification methods. To verify the effectiveness, the proposed method is applied on the QPZZ-II rotating machinery fault simulation rig in which the root crack and the tooth broken faults are introduced into the gearbox’s driving gear. Experimental results show that the fault indicator not only can effectively reveal the health state of the gear, but also is without being influenced by the load fluctuation. And, the accuracy rate of fault diagnosis is over 96 %

Exponential Integrators for Stochastic Maxwell's Equations Driven by It\^o Noise

This article presents explicit exponential integrators for stochastic Maxwell's equations driven by both multiplicative and additive noises. By utilizing the regularity estimate of the mild solution, we first prove that the strong order of the numerical approximation is $\frac 12$ for general multiplicative noise. Combing a proper decomposition with the stochastic Fubini's theorem, the strong order of the proposed scheme is shown to be $1$ for additive noise. Moreover, for linear stochastic Maxwell's equation with additive noise, the proposed time integrator is shown to preserve exactly the symplectic structure, the evolution of the energy as well as the evolution of the divergence in the sense of expectation. Several numerical experiments are presented in order to verify our theoretical findings.Comment: 21 Page

Condition trend prediction of aero-generator based on particle swarm optimization and fuzzy integral

In order to improve and enhance the prediction accuracy and efficiency of aero-generator running trend, grasp its running condition, and avoid accidents happening, in this paper, auto-regressive and moving average model (ARMA) and least squares support vector machine (LSSVM) which are used to predict its running trend have been optimized using particle swarm optimization (PSO) based on using features found in real aero-generator life test, which lasts a long period of time on specialized test platform and collects mass data that reflects aero-generator characteristics, to build new models of PSO-ARMA and PSO-LSSVM. And we use fuzzy integral methodology to carry out decision fusion of the predicted results of these two new models. The research shows that the prediction accuracy of PSO-ARMA and PSO-LSSVM has been much improved on that of ARMA and LSSVM, and the results of decision fusion based on fuzzy integral methodology show further substantial improvement in accuracy than each particle swarm optimized model. Conclusion can be drawn that the optimized model and the decision fusion method presented in this paper are available in aero-generator condition trend prediction and have great value of engineering application

Gene rearrangement analysis and ancestral order inference from chloroplast genomes with inverted repeat

Background Genome evolution is shaped not only by nucleotide substitutions, but also by structural changes including gene and genome duplications, insertions, deletions and gene order rearrangements. The most popular methods for reconstructing phylogeny from genome rearrangements include GRAPPA and MGR. However these methods are limited to cases where equal gene content or few deletions can be assumed. Since conserved duplicated regions are present in many chloroplast genomes, the inference of inverted repeats is needed in chloroplast phylogeny analysis and ancestral genome reconstruction. Results We extend GRAPPA and develop a new method GRAPPA-IR to handle chloroplast genomes. A test of GRAPPA-IR using divergent chloroplast genomes from land plants and green algae recovers the phylogeny congruent with prior studies, while analysis that do not consider IR structure fail to obtain the accepted topology. Our extensive simulation study also confirms that GRAPPA has better accuracy then the existing methods. Conclusions Tests on a biological and simulated dataset show GRAPPA-IR can accurately recover the genome phylogeny as well as ancestral gene orders. Close analysis of the ancestral genome structure suggests that genome rearrangement in chloroplasts is probably limited by inverted repeats with a conserved core region. In addition, the boundaries of inverted repeats are hot spots for gene duplications or deletions. The new GRAPPA-IR is available from http://phylo.cse.sc.ed

Reassessment of Split-Leg Signs in Amyotrophic Lateral Sclerosis: Differential Involvement of the Extensor Digitorum Brevis and Abductor Hallucis Muscles

Background: The muscle patterns involved in the “split-leg” syndrome of amyotrophic lateral sclerosis (ALS) remains controversial. We sought to evaluate and reassess the pattern of the extensor digitorum brevis (EDB) and the abductor hallucis (AH) muscles' involvement in split-leg syndrome in ALS.Methods: We recruited 60 consecutive patients with ALS and 25 healthy controls (HCs). Compound muscle action potentials (CMAPs) and F-waves were recorded over the EDB and AH muscles in all subjects. For comparison, we classified patients into two categories based on the presence or absence of lower limbs symptoms.Results: The EDB/AH CMAP amplitude ratio was significantly reduced in patients with affected legs (0.33 ± 0.21, P = 0.007), whereas patients with unaffected legs had a ratio similar to that of the HCs. The EDB/AH ratios for the F-wave latencies, mean F-wave amplitude, mean F/M amplitude ratio, and the persistence of the total repeater F-wave shapes (index Freps) of the EDB-AH, were significantly increased in the affected leg group, whereas the EDB/AH ratio for F-wave persistence was significantly reduced. These findings indicated a greater loss of lower motor neurons (LMNs) innervating the EDB and dysfunction of spinal motoneurons innervating the EDB. In the unaffected leg group, the EDB, but not the AH, F-wave latencies, mean and maximal F/M amplitude ratios, and index Freps were significantly altered. Receiver operating characteristic curve analysis suggested that the EDB F-wave latencies, mean F/M amplitude ratios, and index Freqs (area under the curve [AUC] > 0.8) more strongly differentiated patients with ALS from the HCs compared to the EDB/AH CMAP amplitude ratio (AUC = 0.61). Notably, the EDB maximal F-wave latency and index Freqs reliably differentiated patients with unaffected legs (HCs), with AUCs of 0.83 (95% CI 0.76–0.91) and 0.81 (95% CI 0.72–0.89), sensitivities of 76 and 78%, and specificities of 76 and 78%, respectively.Conclusions: These results suggest preferential EDB compared to AH involvement in the split-leg syndrome of ALS. The EDB maximal F-wave latency and index Freqs robustly differentiated patients with ALS from HCs, which might facilitate an earlier identification of ALS