Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters

Event-triggered distributed H∞ state estimation with packet dropouts through sensor networks

This study is concerned with the event-triggered distributed H∞ state estimation problem for a class of discrete-time stochastic non-linear systems with packet dropouts in a sensor network. An event-triggered communication mechanism is adopted over the sensor network with hope to reduce the communication burden and the energy consumption, where the measurements on each sensor are transmitted only when a certain triggering condition is violated. Furthermore, a novel distributed state estimator is designed where the available innovations are not only from the individual sensor, but also from its neighbouring ones according to the given topology. The purpose of the problem under consideration is to design a set of distributed state estimators such that the dynamics of estimation errors is exponentially mean-square stable and also the prespecified H∞ disturbance rejection attenuation level is guaranteed. By utilising the property of the Kronecker product and the stochastic analysis approaches, sufficient conditions are established under which the addressed state estimation problem is recast as a convex optimisation one that can be easily solved via available software packages. Finally, a simulation example is utilised to illustrate the usefulness of the proposed design scheme of event-triggered distributed state estimators.This work was supported in part by Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61203139, 61473076, 61374127 and 61422301, the Shanghai Rising-Star Program of China under Grant 13QA1400100, the ShuGuang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 13SG34, the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of Germany

Intraseasonal variation of the East Asian summer monsoon associated with the MJO

We investigate the daily variability of the East Asian summer monsoon (EASM) by projecting daily wind anomaly data onto the two major modes of an interannual multivariate Empirical Orthogonal Functions analysis. Mode 1, closely resembling the Pacific-Japan (PJ) pattern and referred to as PJ-mode, transits from positive to negative phase around mid-summer consistent with the Meiyu rains predominantly being an early summer phenomenon. Mode 2, which is influenced by the Indian summer monsoon (ISM) and referred to as ISM-mode, peaks in late July and early August and is associated with rainfall farther north over China. We then analyze the relation between the intraseasonal variation of the EASM and the Madden-Julian Oscillation (MJO) by analyzing circulation anomalies following MJO events. In the lower troposphere, the circulation anomalies associated with the MJO most strongly project on the PJ-mode. MJO phases 1-4 (5-8) favor the positive (negative) phase of the PJ-mode by favoring the anticyclonic (cyclonic) anomalies over the subtropical western North Pacific. In the upper troposphere, the circulation anomalies associated with the MJO project mainly on the ISM-mode

Electric-field control of magnetism in few-layered van der Waals magnet

Manipulating quantum state via electrostatic gating has been intriguing for many model systems in nanoelectronics. When it comes to the question of controlling the electron spins, more specifically, the magnetism of a system, tuning with electric field has been proven to be elusive. Recently, magnetic layered semiconductors have attracted much attention due to their emerging new physical phenomena. However, challenges still remain in the demonstration of a gate controllable magnetism based on them. Here, we show that, via ionic gating, strong field effect can be observed in few-layered semiconducting Cr$_{2}$Ge$_{2}$Te$_{6}$ devices. At different gate doping, micro-area Kerr measurements in the studied devices demonstrate tunable magnetization loops below the Curie temperature, which is tentatively attributed to the moment re-balance in the spin-polarized band structure. Our findings of electric-field controlled magnetism in van der Waals magnets pave the way for potential applications in new generation magnetic memory storage, sensors, and spintronics.Comment: 8 pages, 4 figure

Development and application of a diagnostic instrument to evaluate secondary school students’ conceptions of electrolysis

A two-tier multiple choice diagnostic instrument consisting of 17 items was developed to evaluate students’ understanding of basic electrolysis concepts. This study which used mixed qualitative and quantitative methods, was conducted in 2006 and 2007 to produce the final instrument. Subsequently, the final instrument was administered to 16 year-old secondary school students (N = 330) who had completed the first year of a two year chemistry course. The instrument was found to have a high Cronbach’s alpha reliability coefficient of 0.85 which is greater than the threshold value of 0.5 quoted by Nunally and Bernstein (1994). Analysis of students’ responses demonstrated good discrimination indices between the top and bottom groups of low- and high-achieving students, with the indices ranging from 0.42 to 0.84 for 16 items and 0.28 for one item. The analysis also identified 29 alternative conceptions that involved a variety of electrolysis concepts relating to the nature and reaction of the electrodes, the migration of ions, the preferential discharge of ions, the products of electrolysis, and changes in the concentration and colour of the electrolyte.In addition, there was a mismatch between students’ confidence in answering the items and their correct responses. Students’ level of confidence in providing correct responses to these items ranged from 44% to 72%, but the actual correct responses ranged from 19% to 53%. As no other similar instrument has been reported in the research literature, this instrument is a convenient diagnostic tool that teachers could use to identify students’ preconceptions prior to introducing the topic. In addition, using the instrument in formative assessment during classroom instruction will enable teachers to identify students’ alternative conceptions and institute appropriate remediation measures with the students concerned

Ultrasonic transducer-guided electrochemical impedance spectroscopy to assess lipid-laden plaques

Plaque rupture causes acute coronary syndromes and stroke. Intraplaque oxidized low density lipoprotein (oxLDL) is metabolically unstable and prone to induce rupture. We designed an intravascular ultrasound (IVUS)-guided electrochemical impedance spectroscopy (EIS) sensor to enhance the detection reproducibility of oxLDL-laden plaques. The flexible 2-point micro-electrode array for EIS was affixed to an inflatable balloon anchored onto a co-axial double layer catheter (outer diameter = 2 mm). The mechanically scanning-driven IVUS transducer (45 MHz) was deployed through the inner catheter (diameter = 1.3 mm) to the acoustic impedance matched-imaging window. Water filled the inner catheter to match acoustic impedance and air was pumped between the inner and outer catheters to inflate the balloon. The integrated EIS and IVUS sensor was deployed into the ex vivo aortas dissected from the fat-fed New Zealand White (NZW) rabbits (n = 3 for fat-fed, n = 5 normal diet). IVUS imaging was able to guide the 2-point electrode to align with the plaque for EIS measurement upon balloon inflation. IVUS-guided EIS signal demonstrated reduced variability and increased reproducibility (p < 0.0001 for magnitude, p < 0.05 for phase at <15 kHz) as compared to EIS sensor alone (p < 0.07 for impedance, p < 0.4 for phase at <15 kHz). Thus, we enhanced topographic and EIS detection of oxLDL-laden plaques via a catheter-based integrated sensor design to enhance clinical assessment for unstable plaque

Achieving λ/10 resolution CW STED nanoscopy with a Ti:Sapphire oscillator

In this report, a Ti:Sapphire oscillator was utilized to realize synchronization-free stimulated emission depletion (STED) microscopy. With pump power of 4.6 W and sample irradiance of 310 mW, we achieved super-resolution as high as 71 nm. With synchronization-free STED, we imaged 200 nm nanospheres as well as all three cytoskeletal elements (microtubules, intermediate filaments, and actin filaments), clearly demonstrating the resolving power of synchronization-free STED over conventional diffraction limited imaging. It also allowed us to discover that, Dylight 650, exhibits improved performance over ATTO647N, a fluorophore frequently used in STED. Furthermore, we applied synchronization-free STED to image fluorescently-labeled intracellular viral RNA granules, which otherwise cannot be differentiated by confocal microscopy. Thanks to the widely available Ti:Sapphire oscillators in multiphoton imaging system, this work suggests easier access to setup super-resolution microscope via the synchronization-free STED. © 2012 Liu et al