Bearing fault diagnosis based on EMD-KPCA and ELM

In recent years, many studies have been conducted in bearing fault diagnosis, which has attracted increasing attention due to its nonlinear and non-stationary characteristics. To solve this problem, this paper proposes, a fault diagnosis method based on Empirical Mode Decomposition (EMD), Kernel Principal Component Analysis (KPCA), and Extreme Learning Machines (ELM) neural network, which combines the existing self-adaptive time-frequency signal processing with the advantages of non-linear multivariate dimensionality reduction KPCA approach and ELM neural network. First, EMD is applied to decompose the vibration signals into a finite number of intrinsic mode functions, in which the corresponding energy values are selected as the initial feature vector. Second, KPCA is used to further reduce the dimensionality for a simplified low-dimension feature vector. Finally, ELM is introduced to classify the extracted fault feature vectors for lessening the human intervention and reducing the fault diagnosis time. Experimental results demonstrate that the proposed diagnostic can effectively identify and classify typical bearing faults

Towards the Characterization of Terminal Cut Functions: a Condition for Laminar Families

We study the following characterization problem. Given a set $T$ of terminals and a $(2^{|T|}-2)$-dimensional vector $\pi$ whose coordinates are indexed by proper subsets of $T$, is there a graph $G$ that contains $T$, such that for all subsets $\emptyset\subsetneq S\subsetneq T$, $\pi_S$ equals the value of the min-cut in $G$ separating $S$ from $T\setminus S$? The only known necessary conditions are submodularity and a special class of linear inequalities given by Chaudhuri, Subrahmanyam, Wagner and Zaroliagis. Our main result is a new class of linear inequalities concerning laminar families, that generalize all previous ones. Using our new class of inequalities, we can generalize Karger's approximate min-cut counting result to graphs with terminals

Hydraulic pump fault diagnosis with compressed signals based on stagewise orthogonal matching pursuit

In recent years, many studies have been conducted in hydraulic pump fault diagnosis, which usually ask for sufficient monitoring data. However, in some engineering applications, such as remote monitoring, it is not always guaranteed due to the limitations of transmission bandwidth and computational resources. To solve this problem, this paper proposes, a fault diagnosis method based on compressive sensing theory, which could guarantee the monitoring data can be compressed, and then recovered in remote side. First, original vibration signal of hydraulic pump is used to structure compressed dictionary matrix. Second, Gaussian random matrix is applied to compress the vibration monitoring data of hydraulic pump. Finally, stagewise orthogonal matching pursuit is introduced to reconstruct test data and classify the compressed test data vectors for lessening the diagnosis error rate and reducing the fault diagnosis time. Experimental results demonstrate that the proposed diagnostic can effectively identify and classify typical hydraulic pump faults

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that frequent shuffles would significantly intensify the overhead. MTD requires a quantitative measure to compare the cost and effectiveness of available adaptations and explore the best trade-off between them. In this paper, therefore, we propose a new cost-effective shuffling method against DDoS attacks using MTD. By exploiting Multi-Objective Markov Decision Processes to model the interaction between the attacker and the defender, and designing a cost-effective shuffling algorithm, we study the best trade-off between the effectiveness and cost of shuffling in a given shuffling scenario. Finally, simulation and experimentation on an experimental software defined network (SDN) indicate that our approach imposes an acceptable shuffling overload and is effective in mitigating DDoS attacks

Different Regular Black Holes: Geodesic Structures of Test Particles

This paper investigates the metric of previously proposed regular black holes, calculates their effective potentials, and plots the curves of the effective potentials. By determining the conserved quantities, the dynamical equations for particles and photons near the black hole are derived. The analysis encompasses timelike and null geodesics in different spacetimes, including bound geodesics, unstable circular geodesics, stable circular geodesics, and escape geodesics. The findings are presented through figures and tables. Furthermore, the bound geodesics of the four regular black hole spacetimes are analyzed, examining the average distance of particle orbits from the center of the event horizon, the precession behavior of the perihelion, and the probability of particles appearing inside the outer event horizon during motion. Based on these analyses, a general formula is proposed, which yields the existing metrics when specific parameter values are chosen. The impact of parameter variations on the effective potential and geodesics is then computed using this new formula.Comment: 23 pages, 13 figure