Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acoustic emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the remaining useful life (RUL) of machine components is required. This model is used to analyze the output signals of a machine whilst in operation and accordingly helps to set an early alarm tool that reduces the untimely replacement of components and the wasteful machine downtime. Recent improvements indicate the drive on the way towards incorporation of prognosis and diagnosis machine learning techniques in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; support vector machine regression, multilayer artificial neural network model and gaussian process regression, to correlate AE features with corresponding natural wear of slow speed bearings throughout series of laboratory experiments. Analysis of signal parameters such as signal intensity estimator and root mean square was undertaken to discriminate individual types of early damage. It was concluded that neural networks model with back propagation learning algorithm has an advantage over the other models in estimating the RUL for slow speed bearings if the proper network structure is chosen and sufficient data is provided.Peer reviewe

Dielectric Breakdown in Chemical Vapor Deposited Hexagonal Boron Nitride

Insulating films are essential in multiple electronic devices because they can provide essential functionalities, such as capacitance effects and electrical fields. Two-dimensional (2D) layered materials have superb electronic, physical, chemical, thermal, and optical properties, and they can be effectively used to provide additional performances, such as flexibility and transparency. 2D layered insulators are called to be essential in future electronic devices, but their reliability, degradation kinetics, and dielectric breakdown (BD) process are still not understood. In this work, the dielectric breakdown process of multilayer hexagonal boron nitride (h-BN) is analyzed on the nanoscale and on the device level, and the experimental results are studied via theoretical models. It is found that under electrical stress, local charge accumulation and charge trapping/detrapping are the onset mechanisms for dielectric BD formation. By means of conductive atomic force microscopy, the BD event was triggered at several locations on the surface of different dielectrics (SiO2, HfO2, Al2O3, multilayer h-BN, and monolayer h-BN); BD-induced hillocks rapidly appeared on the surface of all of them when the BD was reached, except in monolayer h-BN. The high thermal conductivity of h-BN combined with the one-atom-thick nature are genuine factors contributing to heat dissipation at the BD spot, which avoids self-accelerated and thermally driven catastrophic BD. These results point to monolayer h-BN as a sublime dielectric in terms of reliability, which may have important implications in future digital electronic devices.Fil: Jiang, Lanlan. Soochow University; ChinaFil: Shi, Yuanyuan. Soochow University; China. University of Stanford; Estados UnidosFil: Hui, Fei. Soochow University; China. Massachusetts Institute of Technology; Estados UnidosFil: Tang, Kechao. University of Stanford; Estados UnidosFil: Wu, Qian. Soochow University; ChinaFil: Pan, Chengbin. Soochow University; ChinaFil: Jing, Xu. Soochow University; China. University of Texas at Austin; Estados UnidosFil: Uppal, Hasan. University of Manchester; Reino UnidoFil: Palumbo, Félix Roberto Mario. Comisión Nacional de Energía Atómica; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lu, Guangyuan. Chinese Academy of Sciences; República de ChinaFil: Wu, Tianru. Chinese Academy of Sciences; República de ChinaFil: Wang, Haomin. Chinese Academy of Sciences; República de ChinaFil: Villena, Marco A.. Soochow University; ChinaFil: Xie, Xiaoming. Chinese Academy of Sciences; República de China. ShanghaiTech University; ChinaFil: McIntyre, Paul C.. University of Stanford; Estados UnidosFil: Lanza, Mario. Soochow University; Chin

Stochastic sensitivity measure for mistuned high-performance turbines

A stochastic measure of sensitivity is developed in order to predict the effects of small random blade mistuning on the dynamic aeroelastic response of turbomachinery blade assemblies. This sensitivity measure is based solely on the nominal system design (i.e., on tuned system information), which makes it extremely easy and inexpensive to calculate. The measure has the potential to become a valuable design tool that will enable designers to evaluate mistuning effects at a preliminary design stage and thus assess the need for a full mistuned rotor analysis. The predictive capability of the sensitivity measure is illustrated by examining the effects of mistuning on the aeroelastic modes of the first stage of the oxidizer turbopump in the Space Shuttle Main Engine. Results from a full analysis mistuned systems confirm that the simple stochastic sensitivity measure predicts consistently the drastic changes due to misturning and the localization of aeroelastic vibration to a few blades

Wind turbine condition assessment through power curve copula modeling

Power curves constructed from wind speed and active power output measurements provide an established method of analyzing wind turbine performance. In this paper it is proposed that operational data from wind turbines are used to estimate bivariate probability distribution functions representing the power curve of existing turbines so that deviations from expected behavior can be detected. Owing to the complex form of dependency between active power and wind speed, which no classical parameterized distribution can approximate, the application of empirical copulas is proposed; the statistical theory of copulas allows the distribution form of marginal distributions of wind speed and power to be expressed separately from information about the dependency between them. Copula analysis is discussed in terms of its likely usefulness in wind turbine condition monitoring, particularly in early recognition of incipient faults such as blade degradation, yaw and pitch errors

Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis

Systematic network monitoring can be the cornerstone for the dependable operation of safety-critical distributed systems. In this paper, we present our vision for informed anomaly detection through network monitoring and resilience measurements to increase the operators' visibility of ATM communication networks. We raise the question of how to determine the optimal level of automation in this safety-critical context, and we present a novel passive network monitoring system that can reveal network utilisation trends and traffic patterns in diverse timescales. Using network measurements, we derive resilience metrics and visualisations to enhance the operators' knowledge of the network and traffic behaviour, and allow for network planning and provisioning based on informed what-if analysis

A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number

Motivation: cis-regulatory DNA sequence elements, such as enhancers and silencers, function to control the spatial and temporal expression of their target genes. Although the overall levels of gene expression in large cell populations seem to be precisely controlled, transcription of individual genes in single cells is extremely variable in real time. It is, therefore, important to understand how these cis-regulatory elements function to dynamically control transcription at single-cell resolution. Recently, statistical methods have been proposed to back calculate the rates involved in mRNA transcription using parameter estimation of a mathematical model of transcription and translation. However, a major complication in these approaches is that some of the parameters, particularly those corresponding to the gene copy number and transcription rate, cannot be distinguished; therefore, these methods cannot be used when the copy number is unknown. Results: Here, we develop a hierarchical Bayesian model to estimate biokinetic parameters from live cell enhancer–promoter reporter measurements performed on a population of single cells. This allows us to investigate transcriptional dynamics when the copy number is variable across the population. We validate our method using synthetic data and then apply it to quantify the function of two known developmental enhancers in real time and in single cells

A model for availability growth with application to new generation offshore wind farms

A model for availability growth is developed to capture the effect of systemic risk prior to construction of a complex system. The model has been motivated by new generation offshore wind farms where investment decisions need to be taken before test and operational data are available. We develop a generic model to capture the systemic risks arising from innovation in evolutionary system designs. By modelling the impact of major and minor interventions to mitigate weaknesses and to improve the failure and restoration processes of subassemblies, we are able to measure the growth in availability performance of the system. We describe the choices made in modelling our particular industrial setting using an example for a typical UK Round III offshore wind farm. We obtain point estimates of the expected availability having populated the simulated model using appropriate judgemental and empirical data. We show the relative impact of modelling systemic risk on system availability performance in comparison with estimates obtained (Lesley Walls) from typical system availability modelling assumptions used in offshore wind applications. While modelling growth in availability is necessary for meaningful decision support in developing complex systems such as offshore wind farms, we also discuss the relative value of explicitly articulating epistemic uncertainties

Reliability Demonstration Approach for Advanced Stirling Radioisotope Generator

Developed for future space missions as a high-efficiency power system, the Advanced Stirling Radioisotope Generator (ASRG) has a design life requirement of 14 yr in space following a potential storage of 3 yr after fueling. In general, the demonstration of long-life dynamic systems remains difficult in part due to the perception that the wearout of moving parts cannot be minimized, and associated failures are unpredictable. This paper shows a combination of systematic analytical methods, extensive experience gained from technology development, and well-planned tests can be used to ensure a high level reliability of ASRG. With this approach, all potential risks from each life phase of the system are evaluated and the mitigation adequately addressed. This paper also provides a summary of important test results obtained to date for ASRG and the planned effort for system-level extended operation