Application of orthogonal neighborhood preserving projections and two dimensional hidden Markov model for the degradation evaluation of rolling elements bearings

An effective degradation indicator created from the general features is still a hotspot for the condition monitoring of bearing. To cover the shortage of the general features based indicator, some new indicators are built using multiple general features extracted from the original vibration signal without considering the internal relevancy among the features. To address that problem, a new indicator is proposed using the Orthogonal Neighborhood Preserving Projections (ONPP) and 2-Dimensional Hidden Markov Model (2-D HMM). With the ability of keeping the local structure of data set, Orthogonal Neighborhood Preserving Projections is used to obtain the low dimensional features with the main information remained. Unlike 1-Dimensional data-processing algorithm that commonly converts the multiple features into a vector to deal with the high-dimensional data with the integral property of the multiple features considered only, 2-Dimensional Hidden Markov Model not only take the relevance between the individuals of fault features into consideration but also capture the global characteristics of the multiple features. Then a likelihood probability based health assessment indication can be constructed by combing 2-D HMM with the data pre-processed by ONPP. The experiment results indicate that the proposed indicator show great abilities to make degradation performance of the bearing and is sensitive to incipient defects

Hop-Reservation Multiple Access with Variable Slots

AbstractHop-reservation multiple access control protocols in Ad Hoc networks are widely researched for its virtue in anti-jamming. Several typical such protocols are introduced and compared. Based on the analysis about their performance on anti-jamming and ability to serve upper protocols, a hop-reservation multiple access protocol with variable slot (HMAVS) is proposed. By the adaptation of variable length slots, the hop speed of control channel can be supported to the largest extent while diverse applications can be served without additional cost. Simulation results demonstrate the preference of HMAVS to other existing protocols

Rolling element bearing weak fault diagnosis based on spatial correlation and ALIFD

Vibration signals of rolling element bearings during operation are always very complex, random strongly and broadband. Adaptive Local Iterative Filtering Decomposition (ALIFD) can overcome the smoothness and adaptive flaws of Iterative Filtering Decomposition (IFD), but it is so susceptible to random noise that it’s less effective. Here, spatial correlation was proposed. Firstly, the signal was denoised by spatial correlation and decomposed into several modes by ALIFD. Finally, the envelope demodulation was analyzed to extract fault feature. The simulating signal analysis and bearing fault simulator show that this method can be available for separating different frequencies of bearing fault vibration signals

Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks

In complex networks, the failure of one or very few nodes may cause cascading failures. When this dynamical process stops in steady state, the size of the giant component formed by remaining un-failed nodes can be used to measure the severity of cascading failures, which is critically important for estimating the robustness of networks. In this paper, we provide a cascade of overload failure model with local load sharing mechanism, and then explore the threshold of node capacity when the large-scale cascading failures happen and un-failed nodes in steady state cannot connect to each other to form a large connected sub-network. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks, and validate the effectiveness of this method in simulation. This threshold provide us a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks.Comment: 11 pages, 4 figure

Stability bound analysis of singularly perturbed systems with time-delay

This paper considers the stability bound problem of singularly perturbed systems with time-delay. Some stability criteria are derived by constructing appropriate Lyapunov-Krasovskii functionals. The proposed criteria are less conservative than the existing ones. Two numerical examples are given to illustrate the advantages and effectiveness of the proposed methods

PREDICTING BUYERS’ REPURCHASE INTENTIONS IN CROSS-BORDER E-COMMERCE: A VALENCE FRAMEWORK PERSPECTIVE

Cross-border e-commerce has become an important channel for promoting international trade. Yet, the factors influencing buyer behavior in cross-border e-commerce have received relatively less research attention than in domestic e-commerce settings. In this paper we draw on the valence framework to develop and test a research model of buyer repeat purchase intentions in cross-border e-commerce. We hypothesized the effects of positive valences (value, monetary saving, convenience and product offerings) along with negative valences (product and transaction-based uncertainties) on repeat purchase intention. Data was collected from users of a popular cross-border e-commerce provider in China. Results (n=169) revealed that positive valences exert the strongest effects on repeat purchase intention, but negative valences are also significant. These include product-based uncertainties and transaction-based uncertainties. Our model explained 69% of the variance in repeat purchase intentions in a cross-border e-commerce platform. Results enhance our understanding of cross-border e-commerce and have important implications for online providers competing in international markets

Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks

Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critical insights from demonstration data but also demonstrates the capacity to learn a candidate Lyapunov energy function that is consistent with the provided data. The model presented in this paper employs a straightforward neural network architecture that excels in fulfilling a dual objective: optimizing accuracy while simultaneously preserving global stability. To comprehensively evaluate the effectiveness of the proposed algorithm, rigorous assessments are conducted using the LASA dataset, further reinforced by empirical validation through a robotic experiment