Big data and reliability applications: The complexity dimension Yili Hong, Man Zhang and William Q. Meeker
Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges on big data analytics. This article intends to extend that discussion by focusing on how to use data with complicated structures to do reliability analysis. Such data types include high-dimensional sensor data, functional curve data, and image streams. We first provide a review of recent developments, then we provide a discussion on how analytical methods can be developed to tackle the challenging aspects that arise from the complex features of big data in reliability applications. The use of modern statistical methods such as variable selection, functional data analysis, scalar-on-image regression, spatio-temporal data models, and machine-learning techniques will also be discussed.
Predicting field reliability based on two-dimensional warranty data with learning effects By Shuguang He, Zhaomin Zhang, Wei Jiang and Dejun Bian
Understanding the field reliability of a sold product is crucial to both managers and engineers for monitoring product quality and improving warranty service design. When practitioners model warranty data, they it often assume that the lifetimes of products manufactured on different days is homogeneously distributed (i.e., product reliability remains the same over time). Based on a two-dimensional warranty data set collected from an automobile manufacturer, we find that the reliability of products improves over time.
A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes By Abdallah Chehade, Changyue Song, Kaibo Liu, Abhinav Saxena and Xi Zhang
Operating units, in practice, often suffer from multiple modes of failure, and each failure mode has a distinct influence on the service life cycle path of a unit. The rapid development of sensor and communication technologies has enabled multiple sensors to simultaneously monitor and track the health status of a unit in real time. However, one challenging question that remains to be resolved is how to leverage data from multiple sensors for better degradation modeling and prognostic analysis, especially when there are multiple failure modes. A case study that involves the degradation data set of an aircraft gas turbine engine with two potential failure modes is conducted to numerically evaluate the performance of our proposed method compared to other techniques in the related literature.
A statistical modeling approach for spatio-temporal degradation data By Xiao Liu, Kyongmin Yeo and Jayant Kalagnanam
This article investigates the modeling of a new type of degradation data: spatio-temporal degradation data collected from a spatial domain over time. Like existing stochastic degradation models, a random field is constructed to describe the spatio-temporal degradation process. We also show the connection, under special conditions, between the proposed statistical model and a class of physical-degradation processes given by stochastic partial differential equations. Numerical examples are presented to illustrate modeling approach, parameter estimation, model validation, and applications.
Reliability analysis considering dynamic material local deformation By Wujun Si, Qingyu Yang, Xin Wu and Yong Chen
Material deformation is one of the major causes of material failure. In a dynamic deformation process, local deformation—defined as the displacement of various local points on a material—essentially determines the failure. Most existing studies on material reliability are conducted based on either the failure time or the degradation data. The studies do not consider the dynamic local deformation of materials and often are not efficient enough to model the failure mechanism. In this article, we develop reliability analysis by using information contained in the dynamic local deformation of materials in a tensile process.
Uncertainty quantification for monotone stochastic degradation models By Piao Chen and Zhi-Sheng Ye
Degradation data are an important source of product reliability information. Two popular stochastic models for degradation data are the Gamma process and the inverse Gaussian (IG) process, both of which possess monotone degradation paths. Although these two models have been used in numerous applications, the existing interval estimation methods are either inaccurate given a moderate sample size of the degradation data or require a significant computation time when the size of the degradation data is large. To bridge this gap, this article develops a general framework of interval estimation for the Gamma and IG processes based on the method of generalized pivotal quantities.
REGULAR RESEARCH PAPER
Weighted EWMA charts for monitoring type I censored Weibull lifetimes By Shangjie Xu and Daniel R. Jeske
In this article, we first propose a control chart for monitoring the Weibull scale parameter in the context of Type I censored data, assuming that the shape parameter is fixed. The proposed chart is a Shewhart-type chart based on a likelihood ratio test (LRT) that utilizes an exponentially weighted moving average of the log-likelihood. In the literature, this type of chart is called a weighted exponentially weighted moving average (WEWMA) chart. The WEWMA chart is compared with a more standard EWMA chart and a CUSUM chart, which were recently studied as alternative solutions to the monitoring problem.