Feature Evaluation for Effective Bearing Prognostics

Rolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. It is not uncommon to replace a defected/used bearing with a new one that has shorter remaining useful life than the defected one. Thus, the prognostics of bearing plays critical role for increased availability and reduced cost. Effective prognostics highly depends on the quality of the extracted features. Diagnostics is basically a classification problem, whereas prognostics is the process of forecasting the future health states. The quality of the features for classification has been studied thoroughly. However, the evaluation of the quality of features for prognostics is a relatively new problem. This article presents an evaluation method for the goodness of the features for prognostics and presents results on bearings run until failure in a laboratory environment. Copyright © 2012 John Wiley & Sons, Ltd.     

Control Charts for Monitoring Field Failure Data

One responsibility of the reliability engineer is to monitor failure trends for fielded units to confirm that pre‐production life testing results remain valid. This research suggests an approach that is computationally simple and can be used with a small number of failures per observation period. The approach is based on converting failure time data from fielded units to normal distribution data, using simple logarithmic or power transformations. Appropriate normalizing transformations for the classic life distributions (exponential, lognormal, and Weibull) are identified from the literature. Samples of size 500 field failure times are generated for seven different lifetime distributions (normal, lognormal, exponential, and four Weibulls of various shapes). Various control charts are then tested under three sampling schemes (individual, fixed, and random) and three system reliability degradations (large step, small step, and linear decrease in mean time between failures (MTBF)). The results of these tests are converted to performance measures of time to first out‐of‐control signal and persistence of signal after out‐of‐control status begins. Three of the well‐known Western Electric sensitizing rules are used to recognize the assignable cause signals. Based on this testing, the ―X‐chart with fixed sample size is the best overall for field failure monitoring, although the individual chart was better for the transformed exponential and another highly‐skewed Weibull. As expected, the linear decrease in MTBF is the most difficult change for any of the charts to detect. Copyright © 2005 John Wiley & Sons, Ltd.     

Theory and Applications of Hazard Plotting for Censored Failure Data

This paper presents theory and applications of a simple graphical method, called hazard plotting, for the analysis of multiply censored life data consisting of failure times of failed units intermixed with running times on unfailed units. Applications of the method are given for multiply censored data on service life of equipment, for strength data on an item with different failure modes, and for biological data multiply censored on both sides from paired comparisons. Theory for the hazard plotting method, which is based on the hazard function of a distribution, is developed from the properties of order statistics from Type II multiply censored samples.     

Theory and Applications of Hazard Plotting for Censored Failure Data

This paper presents theory and applications of a simple graphical method, called hazard plotting, for the analysis of multiply censored life data consisting of failure times of failed units intermixed with running times on unfailed units. Applications of the method are given for multiply censored data on service life of equipment, for strength data on an item with different failure modes, and for biological data multiply censored on both sides from paired comparisons. Theory for the hazard plotting method, which is based on the hazard function of a distribution, is developed from the properties of order statistics from Type II multiply censored samples.     

减速器主动齿轮断裂失效研究

本文对减速器主动齿轮断裂行为进行了分析。结果表明,齿根倒圆与齿面交接处部位的两长裂纹由大应力造成,裂纹分布、走向及扩展均具有节径型共振特征。辐板与齿轮外圆端面交接处的两小裂纹与二者之间的组织应力有关。     

Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.     

Field Failure Data: an Alternative Proposal for Reliability Estimation

Product reliability is a very important issue for the competitive strategy of industries. In order to estimate a product's reliability, parametric inferential methods are required to evaluate survival test data, which happens to be a fairly expensive data source. Such costly information usually imposes additional compromises in the product development and new challenges to be overcome throughout the product's life cycle. However, manufacturers also keep field failure data for warranty and maintenance purposes, which can be a low‐cost data source for reliability estimation. Field‐failure data are very difficult to evaluate using parametric inferential methods due to their small and highly censored samples, quite often representing mixed modes of failure. In this paper a method for reliability estimation using field failure data is proposed. The proposal is based on the use of non‐parametric inferential methods, associated with resampling techniques to derive confidence intervals for the reliability estimates. Test results show the adequacy of the proposed method to calculate reliability estimates and their confidence interval for different populations, including cases with highly right‐censored failure data. The method is shown to be particularly useful when the sampling distribution is not known, which happens to be the case in a large number of practical reliability evaluations. Copyright © 2006 John Wiley & Sons, Ltd.     

Estimation Techniques for Common Cause Failure Data With Different System Sizes

Modeling of system lifetimes becomes more complicated when external events can cause the simultaneous failure of two or more system components. Models that ignore these common cause failures lead to methods of analysis that overestimate system reliability. Typical data consist of observed frequencies in which i out of m (identical) components in a system failed simultaneously, i = 1,…, m. Because this attribute data is inherently dependent on the number of components in the system, procedures for interpretation of data from different groups with more or fewer components than the system under study are not straightforward. This is a recurrent problem in reliability applications in which component configurations change from one system to the next. For instance, in the analysis of a large power-supply system that has three stand-by diesel generators in case of power loss, statistical tests and estimates of system reliability cannot be derived easily from data pertaining to different plants for which only one or two diesel generators were used to reinforce the main power source. This article presents, discusses, and analyzes methods to use generic attribute reliability data efficiently for systems of varying size.     

面向高保真再现的多光谱图像降维方法

目的研究满足面向高保真再现要求的多光谱图像降维方法。方法基于二进制小波对信号的分解与人类的视觉特性相匹配,以及非负主成分分析法可较好地保证降维的光谱精度,提出采用基于离散二进制小波变化与非负主成分分析法的综合降维方法,并基于多光谱图像高保真再现的光谱精度、色度精度与变光源色差稳定性的要求,提出采用CIELAB的标准色差ab?E、光谱保真度和平均梯度等3个指标来评价降维效果。结果经过多光谱图像的测试实验,基于离散小波变换和非负主成分分析法的综合降维方法相对于其他3种方法,其光谱精度、色度精度和图像清晰度保持良好。结论该方法较好地实现了多光谱图像的高保真再现问题,并且为颜色视觉的认知过程提供了新的理论解释。     

Modeling Discrete Bivariate Data with Applications to Failure and Count Data

In this study, we propose a new class of flexible bivariate distributions for discrete random variables. The proposed class of distribution is based on the notion of conditional failure rate for a discrete‐type random variable. We derive general formulae for the joint distributions belonging to the proposed class that, unlike other discrete bivariate models already proposed in the literature such as the well‐known and most popular Holgate's bivariate Poisson distribution, can model both positive and negative dependence. We discuss general statistical properties of the proposed class as well. Specific families of bivariate distributions can be generated from the general class proposed in this paper just by specifying the ‘baseline distributions’. Furthermore, specific discrete bivariate distributions belonging to the proposed class are applied to analyze three real data sets, and the results are compared with those obtained from conventional models. Copyright © 2017 John Wiley & Sons, Ltd.     

Dimensionality effects of carbon-based thermal additives for microporous adsorbents

We present a systematic study of carbon nanomaterials with different geometries and thermal properties, including few-layer graphene (FLG), graphene oxide (GO), and functionalized carbon nanotubes (fCNT) as additives to enhance the thermal conductivity of microporous adsorbent materials. The dimensionality and intrinsic thermal conductivity of the additives were found to be critical for both maximizing the thermal conductivity enhancement, and minimizing the reduction in the adsorption capacity of the active materials. We demonstrated that two-dimensional (2D) FLG was the most effective thermal additive for zeolite (ZT) adsorbents due to its high thermal conductivity and preferential 2D geometry. Meanwhile, negligible enhancement was observed from one-dimensional (1D) additives such as fCNTs, which is consistent with the predictions from a modified effective medium analysis (EMA). Our work provides insights for the development of additives to enhance the thermal performance of porous materials in applications such as adsorption heat pumps, gas storage, and separation processes.     

Dimensionality reduction for more stable vision parameter estimation

The problem of estimating parameters from data is considered for a class of multi-objective models of importance in computer vision. One previous approach for solving the problem is via the fundamental numerical scheme (FNS). Here, a more stable version of FNS is developed, with better convergence properties than the original version. The improvement in performance is achieved by reducing the original estimation problem to a couple of problems of lower dimension. By way of example, the new algorithm is applied to the problem of estimating the trifocal tensor relating three views of a scene. Experiments carried out with both synthetic and real images reveal the new estimator to be more stable compared to the original FNS method, and commensurate in accuracy with, but faster than, the gold standard maximum likelihood estimator.     

指数分布场合基于竞争失效数据的参数估计

讨论了指数分布场合竞争失效产品在恒定应力加速寿命试验和步进应用加速寿命试验情形加速方程中有关参数的加权最小二乘估计，利用Monte Carlo方法和参数的最大似然估计作了模拟比较。     

A Generalized Procedure for Monitoring Right‐censored Failure Time Data

Right‐censored failure time data is a common data type in manufacturing industry and healthcare applications. Some control charting procedures were previously proposed to monitor the right‐censored failure time data under some specific distributional assumptions for the observed failure times and censoring times. But these assumptions may not be always satisfied in the real‐world data. Therefore, a more generalized control chart technique, which can handle different types of distributions of the data, is highly needed. Considering the limitations of existing methodologies for detecting changes of hazard rate, this paper develops a generalized statistical procedure to monitor the failure time data in the presence of random right censoring when abundant historical failure times are available. The developed method makes use of the one‐sample nonparametric rank tests without any specific assumptions of the data distribution. The operating characteristic functions of the control chart are derived on the basis of the asymptotic properties of the rank statistics. Case studies are presented to show the effectiveness of the proposed control chart technique, and its performance is investigated and compared with some Shewhart‐type control charts based on the conditional expected value weight. Copyright © 2014 John Wiley & Sons, Ltd.     

Machine Learning with Dimensionality Reduction for DDoS Attack Detection

With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However, the challenge in using these techniques is the limitations on capacity for the volume of data and the required processing time. In this research work, we propose the framework of reducing the dimensions of the data by selecting the most important features which contribute to the predictive accuracy. We show that the ‘lite’ model trained on reduced dataset not only saves the computational power, but also improves the predictive performance. We show that dimensionality reduction can improve both effectiveness (recall) and efficiency (precision) of the model as compared to the model trained on ‘full’ dataset.     

公园声景观感知属性维度数实验研究

以城市公园声环境为研究对象,研究影响声景观感知属性的本质因素.通过现场调查研究了影响声景观评价的各种因素;选取及模拟典型声景观片段,在实验室内采用成对比较法进行主观评价实验,得到关于各声景观素材的不相似度矩阵;再应用多维尺度分析,获得声景观评价影响因素的维度数.为进一步探讨声景观形成机理、实现声景观客观评价及建模提供了理论依据.     

Design of a Prognostics and Health Management System for Electromechanical Equipment Through Time Stress Analysis

Time stress includes all kinds of environment and operating stress such as shock, vibration, temperature and electric current that the electromechanical system suffers in the manufacture, transport and operating process. In this paper, the conception of time stress and prognostics and health management (PHM) system are introduced. Then, in order to improve the false alarm recognition and fault prediction capabilities of the electromechanical equipment, a novel PHM architecture for electromechanical equipment is put forward based on a built-in test (BIT) system design technology and time stress analysis method. Finally, the structure, the design and implementing method and the functions of each module of this PHM system are described in detail.