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

Spin asymmetry and dipole moments in τ\tau-pair production with ultraperipheral heavy ion collisions

The anomalous magnetic (MDM) and electric (EDM) dipole moments of the $\tau$ lepton serve as crucial indicators of new physics beyond the Standard Model. Leveraging azimuthal angular asymmetry as a novel tool in ultraperipheral collisions (UPCs), we attain unparalleled precision in the study of these key properties. Driven by the highly linear polarization of coherent photons, this method uniquely enables both the MDM and EDM to contribute to the $\cos2\phi$ angular distribution in similar magnitudes. Importantly, our approach substantially narrows the parameter space, excluding more than half of it compared to expected UPC-based measurements reliant solely on the total cross-section. This method not only provides improved constraints but also minimizes the need for additional theoretical assumptions.Comment: 6 pages, 3 figure

Building Cooperation in VoIP Network through a Reward Mechanism

In this paper, for solving the moral hazard problem of super nodes in VOIP network and achieving better communication quality, we establish a reward mechanism based on classical efficiency-wage models. In the reward mechanism, the function of reward is to encourage super nodes to contribute their bandwidth and cover their effort costs, whereas the function of fine is to prevent opportunistic super nodes from shirking. We consider that network quality and idle bandwidth are the essential criterions for selecting qualified super nodes. Once all super nodes can satisfy specific conditions, the required reward can be derived so as to improve the VoIP platform\u27s revenue. Moreover, we also suggest several targets both in technical and economic view that the platform provider can strive in order to boost his/her market share. In addition, the case of Skype is discussed in this study and we also examine its current pricing strategy

Dynamical-Corrected Nonadiabatic Geometric Quantum Computation

Recently, nonadiabatic geometric quantum computation has been received great attentions, due to its fast operation and intrinsic error resilience. However, compared with the corresponding dynamical gates, the robustness of implemented nonadiabatic geometric gates based on the conventional single-loop scheme still has the same order of magnitude due to the requirement of strict multi-segment geometric controls, and the inherent geometric fault-tolerance characteristic is not fully explored. Here, we present an effective geometric scheme combined with a general dynamical-corrected technique, with which the super-robust nonadiabatic geometric quantum gates can be constructed over the conventional single-loop and two-loop composite-pulse strategies, in terms of resisting the systematic error, i.e., $\sigma_x$ error. In addition, combined with the decoherence-free subspace (DFS) coding, the resulting geometric gates can also effectively suppress the $\sigma_z$ error caused by the collective dephasing. Notably, our protocol is a general one with simple experimental setups, which can be potentially implemented in different quantum systems, such as Rydberg atoms, trapped ions and superconducting qubits. These results indicate that our scheme represents a promising way to explore large-scale fault-tolerant quantum computation.Comment: 10 pages, 9 figure

Design and Implementation of Service-Oriented Expert System

In recent years, the Internet technologies are well developed and the Internet is filled with all kinds of information. Since the data storage is increasingly distributed and data formats are more diverged, data collection and integration for providing value- added services have gradually become important topics. In this study, we propose the Service-Oriented Expert System (SOES) based on Service Component Architecture (SCA) which can make the services on different platforms turn into a common service component on the Internet, concatenate all the service components by combining with the Enterprise Service Bus (ESB), and use both expert rules and data mining techniques to perform the data classification. The SOES is applied to analyze the annual financial information derived from electronic industry in the Taiwan Economic Journal (TEJ) during 2006 to 2008 for discovering the financial crisis enterprises. The experiment results show that using expert rules and decision tree to find the financial crisis enterprise is higher performance

Artificial Intelligent Diagnosis and Monitoring in Manufacturing

The manufacturing sector is heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. It has been reported that 35% of US manufacturers are currently collecting data from sensors for manufacturing processes enhancement. Nevertheless, many are still struggling to achieve the 'Industry 4.0', which aims to achieve nearly 50% reduction in maintenance cost and total machine downtime by proper health management. For increasing productivity and reducing operating costs, a central challenge lies in the detection of faults or wearing parts in machining operations. Here we propose a data-driven, end-to-end framework for monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to extract hidden degradation features from noisy data. We demonstrate the proposed framework on several representative experimental manufacturing datasets drawn from a wide variety of applications, ranging from mechanical to electrical systems. Results reveal that the framework performs well in all benchmark applications examined and can be applied in diverse contexts, indicating its potential for use as a critical corner stone in smart manufacturing

A QoS-Based Services Selected Method in Service-Oriented Architectures Using Ant Colony System - A Case Study of Airflights

Semantic web is becoming more and more popular these days, and it’s an opportune moment to look at the field’s current state and future opportunities. However, most researchers focus on only one single service recommend from semantic web inference. In some situations, the Multi-Services which are combined many complex services from various service providers are better than single service. The designed Multi-Services Semantic Search System (MS4), which provides the cooperation web-based platform for all related mobile users and service providers, could strengthen the ability of Multi-Services suggestion. In this research, MS4 chooses the adaptable airflight as a case study. MS4 is a five-components system composed of the Mobile Users (MUs), UDDI Registries (UDDIRs), Service Providers (SPs), Semantic Web Services Server (SWSS), and Database Server (DS). Using SOA, OWL-S to build semantic web environment to inference user’s requirements and search various web services which are published in UDDI through the communication networks include internet and 3G/GPRS/GSM mobile networks. In this airline case, we propose the Adaptive Airflights Inference Module (AAIM) combined QoS-Based Services Selected Method (QBSSM) using Ant Colony System (ACS) to reference the adaptable airflights to MUs

Universal critical properties of the Eulerian bond-cubic model

We investigate the Eulerian bond-cubic model on the square lattice by means of Monte Carlo simulations, using an efficient cluster algorithm and a finite-size scaling analysis. The critical points and four critical exponents of the model are determined for several values of $n$. Two of the exponents are fractal dimensions, which are obtained numerically for the first time. Our results are consistent with the Coulomb gas predictions for the critical O($n$) branch for $n < 2$ and the results obtained by previous transfer matrix calculations. For $n=2$, we find that the thermal exponent, the magnetic exponent and the fractal dimension of the largest critical Eulerian bond component are different from those of the critical O(2) loop model. These results confirm that the cubic anisotropy is marginal at $n=2$ but irrelevant for $n<2$