Wind energy has turned into a massive contender of traditional fossil fuel energy. Due to environmental conditions and over-time run conditions, wind turbine blades are prompt to different vibrations which cause damage to the blades. This paper presents an algorithmic classification of various blade fault conditions like blade bend, blade cracks, blade erosion, hub-blade loose connection and pitch angle twist using vibration signals. Initially histogram features were extracted from the vibration data and classified using machine learning algorithms like hyperpipes (HP) and voting feature intervals (VFI) algorithm. The performance of these algorithms were compared with respect to classification accuracy and better algorithm was suggested for fault prediction on wind turbine blades.

Submitted on January 1, 2017; Revised on March 28, 2017; Accepted on April 12, 2017
References: 26

Speech separation is the process of separating the target speech and noise from the noisy speech mixture. Speech separation algorithms are useful in improving the quality and intelligibility of the speech. The various traditional speech separation algorithms such as spectral-subtractive algorithms, Wiener filtering, statistical model-based methods and subspace algorithms are mainly focus on improving the speech quality. But there are applications such as mobile communication, air ground communication and hearing aids, needs speech intelligibility than speech quality. In order to satisfy the requirements of intelligibility, this work proposes an algorithm using Computational Auditory Scene Analysis (CASA) and Support Vector Machine (SVM) to separate the noisy speech into target speech and noise and at the same time improves the speech intelligibility. The proposed algorithm decomposes the clean speech and noise into time-frequency units (T-F) and computes the energy from each frame of clean speech and noise to train the SVM. Latter in the testing phase, the trained SVM is used to estimate the binary mask from the energy of the noisy speech based on whether each T-F unit is dominated by speech or noise. The estimated mask by SVM is used to synthesize the speech signal and is presented to normal-hearing listeners with different age groups to measure the performance of the proposed algorithm. The experimental results show substantial improvements in recognition score because the separated speech has reasonable speech intelligibility.

Submitted on December 14, 2016; Revised on March 8, 2017; Accepted on March 16, 2017
References: 12

MIMO wireless communication systems have extensively been employed due to their ability to transfer data efficiently even at higher data rates and are often being used to increase either the multiplexing gain and/or the diversity gain with a tradeoff. The Diversity-Multiplexing Tradeoff (DMT) had been proven to be a powerful evaluation and comparison tool for the existing and new technologies. However, Throughput-Reliability Tradeoff (TRT) has the notable advantage of revealing the interplay among transmission rate, signal-to-noise ratio and outage/error probability parameters.
This paper proposes TRT analysis of MIMO channels under broad class of fading distributions that includes, different identical fading distributions, correlation between channels, non-identical fading distributions, and non-zero channel means. The analysis is carried out by characterizing the joint pdf of the eigenvalues of Gram matrix in high SNR regime. This work also investigates the relation between DMT and TRT of MIMO in broad class of fading distributions. Further, our study sheds light on different channel parameters and their usefulness in TRT analysis, such as, error/outage probability vs. Signal-to-Noise Ratio (SNR) curves, amount of SNR increase to increase transmission rate and/or decrease error/outage probability, etc.

Submitted on March 9, 2017; First Revised on April 2, 2017; Second Revised on April 6, 2017; Accepted on April 9, 2017
References: 21

This paper proposes a new model that generalizes the traditional Markov repairable system to the case of spatial dependence among components. The components of the system are identical and arranged in two lines and consist of a lattice. The performance of each component depends on its spatial “neighbours” and the number of failed components in other lines. Markov process is adopted to model the performance of the system. The state space and transition rate matrix corresponding to a 6-component lattice load-sharing system with spatial dependence are presented. Availability of the system is obtained via Markov theory and Laplace transform method. A numerical example is given to illustrate the results in this paper. The states of the system are partitioned into four state sets: security, degraded, warning, and failed. The probabilities of visiting to four state sets are also discussed in the numerical example. The work might provide a basis for the reliability analysis of load-sharing systems with interacting components that themselves be arranged in some two-dimensional spatial pattern.

Submitted on November 13, 2016; Revised on April 17, 2017; Accepted on April 24, 2017
References: 16

Cloud-RAID (Redundant Array of Independent Disks) is a data storage model, where different data redundancy techniques (corresponding to different levels) are utilized to enhance data reliability and availability in an anytime, anywhere data access framework implemented in the cloud environment. Such a fault-tolerant system can be subject to imperfect coverage due to imperfect fault detection or recovery mechanism, causing extensive damage to the whole system. In this paper, we model reliability of a cloud-RAID 6 storage system addressing effects of two types of imperfect coverage (element level coverage and fault level coverage). Numerical results are provided to illustrate effects of those behaviors on the system reliability performance.

Submitted on February 23, 2017; Revised on April 20, 2017; Accepted on April 22, 2017
References: 25

Degradation modeling is an effective approach for reliability assessment, remaining useful life prediction, maintenance planning, and prognostics health management. Degradation models are usually developed based on degradation data and/or prior understandings of physics behind degradation processes of products or systems. Further, the effects of environmental or operational conditions on degradation processes and the knowledge about the dependency between degradation processes help improve the explanatory capabilities of degradation models. This paper presents a comprehensive review of existing degradation modeling approaches commonly used in engineering applications. To assist practitioners in understanding the concept of degradation modelling, the existing methods are classified into two broad categories: the data-driven approach and physics-based modelling approach. By systematically reviewing these approaches, we highlight their merits, useful applications and limitations. Finally, we provide a summary and indicate several future research challenges in this important area of reliability engineering.

Submitted on February 22, 2017; Revised on April 22, 2017; Accepted on April 24, 2017
References: 126

A beam finite element model (FEM) for efficient analysis of the mechanical behavior of wire strands is presented. Two-noded elastic-plastic beam elements were used for wire discretization. Hertz contact theory was implemented via newly developed node-to-node compression-only contact element to simulate the wire-to-wire contacts. The first numerical example demonstrated is the analysis of a three layered 19-wire strand under axial tensile load. The results showed excellent agreement with those obtained from the accurate full three-dimensional solid FEM of Jiang et al. The degrees of freedom for the beam FEM is only about 4% of the solid FEM. The second verification example presented is the simulation of a six-layered 91-wire strand under axial tensile load. For the global behavior of the strand, the finite element results showed better agreement with the experimental data than Costello’s elasticity theory. For this multilayered strand, it could be extremely hard to implement an accurate full three-dimensional solid FEM.

Received on July 3, 2016; Revised on November 28, 2016; Accepted on April 8, 2017
References: 10

Revolute joints and sliding joints are essential mechanical elements connecting moving parts of machineries. When implementing the whole machine/global modeling, the connection joints and their local detailed geometric features are usually simplified in order to improve the computational efficiency and avoid convergence difficulties. These kinds of simplification strategies may lead to the problem that the accurate stress in the vicinity of the simplified local regions could not be obtained from the analysis of the global model. In such cases, subsequent sub-modeling analyses are usually employed to obtain the accurate stress results in these regions. Simplification of the local features in the model may sometimes result in significant changes in stiffness of these local regions. When traditional interpolated displacement type of boundary conditions are used to indirectly apply loading to the sub-model, the actual loading added to the sub-model boundary could be much different to the loading derived from the global model due to the stiffness error of the global model. In contrast to applying the displacement boundary condition, a method of directly implementing forces to the sub-model cut-boundaries has been proposed in this paper. These forces applied to the cut-boundaries could be obtained from the analysis results of the global model. Numerical analysis results have shown that the newly proposed sub-modeling technique can load the model more accurately than the traditional sub-model method and the analysis accuracy is not sensitive to the degree of simplification of the global model. The predicted stress results for a telescopic boom of a truck-mounted crane have also been validated by experimental results.

Received on August 1, 2016; Revised on December 21, 2016; Accepted on April 1, 2017
References: 11

Parallel computing is an important approach to achieve a high throughput of serving user requests, which has significant influence on improving performance. Parallel computing service can be realized by hosting multiple copies of the software that performs the same service tasks on different physical machines running in parallel. However, the execution of the software may be interrupted by various kinds of failures, including software failures, hardware failures, and common cause failures (CCF) of co-located copies of the software caused by the failures of the host machine. To analyze the performability of a parallel service, unexpected change of performance caused by random failures and subsequent process of recovery should be counted. This paper presents a theoretical modeling approach encompassing Markov reward models to analyze the performability of a parallel service, which considers software failures, hardware failures, and common cause failures to ensure high fidelity. Simulation results are illustrated to verify the new model.

Submitted on January 22, 2017; First Revised on March 1, 2017; Final Rivised on April 18, 2017; Accepted on April 19, 2017
References: 5

Software reliability growth models (SRGMs) have evolved from describing fault detection process (FDP) into incorporating fault correction process (FCP) as well. Restricted by mathematical tractability, analytical models are facing difficulties for more accurate description the real world situations, e.g. debuggers being different in terms of debugging capabilities and experiences. In this paper, a simulation approach is proposed to model FDP and FCP together considering debuggers with different contributions to the fault detection rate and different fault correction time.

Submitted on August 13, 2016; First Revised on October 30, 2016; Second Revised on November 1, 2016; Accepted on November 10, 2016
References: 14