Big data and reliability applications: The complexity dimension

ABSTRACT Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges on big data analytics. Meeker and Hong (2014; Quality Engineering, pp. 102–16) provided an extensive discussion of the opportunities and challenges on big data and reliability; they also described engineering systems which generate big data that can be used in reliability analysis. Meeker and Hong (2014) focused on large-scale system operating and environment data (i.e., high-frequency multivariate time series data) and provided examples on how to link such data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. 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 in those directions, 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.

[1]  Mary C. Meyer.  INFERENCE USING SHAPE-RESTRICTED REGRESSION SPLINES , 2008, 0811.1705.

[2]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[3]  Yili Hong,et al.  Product Component Genealogy Modeling and Field‐failure Prediction , 2017, Qual. Reliab. Eng. Int..

[4]  Shuguang He,et al.  Predicting field reliability based on two-dimensional warranty data with learning effects , 2018 .

[5]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[6]  Xi Zhang,et al.  A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes , 2018 .

[7]  Nagi Gebraeel,et al.  Multistream sensor fusion-based prognostics model for systems with single failure modes , 2017, Reliab. Eng. Syst. Saf..

[8]  J. Bert Keats,et al.  Statistical Methods for Reliability Data , 1999 .

[9]  Yong Chen,et al.  Reliability analysis considering dynamic material local deformation , 2018 .

[10]  Zhengqiang Pan,et al.  Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes , 2011, Reliab. Eng. Syst. Saf..

[11]  Yili Hong,et al.  Bayesian Life Test Planning for Log-Location-Scale Family of Distributions , 2015 .

[12]  William Q. Meeker,et al.  Accelerated Destructive Degradation Tests: Data, Models, and Analysis , 2003 .

[13]  Nagi Gebraeel,et al.  Degradation modeling applied to residual lifetime prediction using functional data analysis , 2011, 1107.5712.

[14]  Jerald F. Lawless,et al.  Monitoring Warranty Claims With Cusums , 2012, Technometrics.

[15]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[16]  C. Joseph Lu,et al.  Using Degradation Measures to Estimate a Time-to-Failure Distribution , 1993 .

[17]  W. Meeker.  Accelerated Testing: Statistical Models, Test Plans, and Data Analyses , 1991 .

[18]  Yili Hong,et al.  Reliability Meets Big Data: Opportunities and Challenges , 2014 .

[19]  Yili Hong,et al.  Planning Fatigue Tests for Polymer Composites , 2016 .

[20]  Masahiro Yokoyama,et al.  A Study on Estimation of Lifetime Distribution with Covariates Using Online Monitoring , 2015 .

[21]  Xin Wu,et al.  A distribution-based functional linear model for reliability analysis of advanced high-strength dual-phase steels by utilizing material microstructure images , 2017 .

[22]  Kazuyuki Suzuki,et al.  Lifetime Prediction of Vehicle Components Using Online Monitoring Data , 2015 .

[23]  Hiromitsu Hama,et al.  Reliability and availability measures for Internet of Things consumer world perspectives , 2016, 2016 IEEE 5th Global Conference on Consumer Electronics.

[24]  Jie Li,et al.  Multivariate frailty models for multi-type recurrent event data and its application to cancer prevention trial , 2016, Comput. Stat. Data Anal..

[25]  Xiao Wang,et al.  Generalized Scalar-on-Image Regression Models via Total Variation , 2017, Journal of the American Statistical Association.

[26]  Yili Hong,et al.  Semiparametric Models for Accelerated Destructive Degradation Test Data Analysis , 2015, Technometrics.

[27]  William Q. Meeker,et al.  A Statistical Method for Crack Detection from Vibrothermography Inspection Data , 2012 .

[28]  Christian P. Robert,et al.  Statistics for Spatio-Temporal Data , 2014 .

[29]  Emmanuel Yashchin.  Design and Implementation of Systems for Monitoring Lifetime Data , 2012 .

[30]  Loon Ching Tang,et al.  Analysis of Field Return Data With Failed-But-Not-Reported Events , 2018, Technometrics.

[31]  G. A. Whitmore,et al.  Failure Inference From a Marker Process Based on a Bivariate Wiener Model , 1998, Lifetime data analysis.

[32]  Yili Hong,et al.  Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data , 2015, Technometrics.

[33]  Xiao Liu,et al.  A statistical modeling approach for spatio-temporal degradation data , 2018 .

[34]  W. J. Padgett,et al.  Accelerated Degradation Models for Failure Based on Geometric Brownian Motion and Gamma Processes , 2005, Lifetime data analysis.

[35]  Yada Zhu,et al.  Parametric Estimation for Window Censored Recurrence Data , 2014, Technometrics.

[36]  Christine M. Anderson-Cook.  Opportunities to empower statisticians in emerging areas , 2015 .

[37]  W. Nelson.  Statistical Methods for Reliability Data , 1998 .

[38]  Bill Ravens,et al.  An Introduction to Copulas , 2000, Technometrics.

[39]  Liangwei Zhang.  Big Data analytics for eMaintenance: modeling of high-dimensional data streams , 2015 .

[40]  P. Walmsley,et al.  Statistical Method , 1923, Nature.

[41]  Xiao Wang,et al.  An Inverse Gaussian Process Model for Degradation Data , 2010, Technometrics.

[42]  Ana-Maria Staicu,et al.  Longitudinal functional data analysis , 2015, Stat.

[43]  Deovrat Kakde,et al.  Leveraging unstructured data to detect emerging reliability issues , 2015, 2015 Annual Reliability and Maintainability Symposium (RAMS).

[44]  Yili Hong,et al.  Field-Failure Predictions Based on Failure-Time Data With Dynamic Covariate Information , 2013, Technometrics.

[45]  C. Laymon.  A. study , 2018, Predication and Ontology.

[46]  Zhi-Sheng Ye,et al.  Uncertainty quantification for monotone stochastic degradation models , 2018 .

[47]  Nan Chen,et al.  The Inverse Gaussian Process as a Degradation Model , 2014, Technometrics.

[48]  Hongtu Zhu,et al.  Tensor Regression with Applications in Neuroimaging Data Analysis , 2012, Journal of the American Statistical Association.

[49]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[50]  William Q. Meeker,et al.  Linking Accelerated Laboratory Test with Outdoor Performance Results for a Model Epoxy Coating System , 2008 .

[51]  William Q. Meeker,et al.  Early Detection of Reliability Problems Using Information From Warranty Databases , 2002, Technometrics.

[52]  Jianjun Shi,et al.  A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis , 2013, IEEE Transactions on Automation Science and Engineering.

[53]  David M. Steinberg,et al.  Industrial statistics: The challenges and the research , 2016 .

[54]  Om Prakash Yadav,et al.  Reliability estimation considering usage rate profile and warranty claims , 2016 .

[55]  Nagi Gebraeel,et al.  A Functional Time Warping Approach to Modeling and Monitoring Truncated Degradation Signals , 2014, Technometrics.

[56]  Julien Jacques,et al.  Model-based clustering for multivariate functional data , 2013, Comput. Stat. Data Anal..

[57]  Loon Ching Tang,et al.  Reliability analysis and spares provisioning for repairable systems with dependent failure processes and a time-varying installed base , 2016 .

[58]  Yili Hong,et al.  A Multi-Level Trend-Renewal Process for Modeling Systems With Recurrence Data , 2017, Technometrics.

[59]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[60]  Haitao Liao,et al.  Reliability Analysis with Multiple Dependent Features from a Vibration-Based Accelerated Degradation Test , 2016 .

[61]  Ran Jin,et al.  Nonlinear general path models for degradation data with dynamic covariates , 2016 .

[62]  R. Tibshirani,et al.  The solution path of the generalized lasso , 2010, 1005.1971.

[63]  Masahiro Yokoyama,et al.  A Study on Estimation of Lifetime Distribution with Covariates Under Misspecification for Baseline Distribution , 2016 .

[64]  N. Balakrishnan,et al.  Bivariate degradation analysis of products based on Wiener processes and copulas , 2013 .

[65]  G A Whitmore,et al.  Estimating degradation by a wiener diffusion process subject to measurement error , 1995, Lifetime data analysis.

[66]  Ji Zhu,et al.  Variable Selection for Model‐Based High‐Dimensional Clustering and Its Application to Microarray Data , 2008, Biometrics.

[67]  Chien-Yu Peng,et al.  Inverse Gaussian Processes With Random Effects and Explanatory Variables for Degradation Data , 2015, Technometrics.

[68]  Xiaoyang Li,et al.  Stochastic Modeling and Analysis of Multiple Nonlinear Accelerated Degradation Processes through Information Fusion , 2016, Sensors.