Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation...

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1472&context=lkcsb_research

Residual-life distributions from component degradation signals: A Bayesian approach

As an alternative to traditional life testing, degradation tests can be effective in assessing product reliability when measurements of degradation leading to failure can be observed. This article presents a degradation model for highly reliable light displays, such as plasma display panels and vacuum fluorescent displays (VFDs). Standard degradation models fail to capture the burn-in characteristics of VFDs, when emitted light actually increases up to a certain point in time before it decreases (or degrades) continuously. Random coefficients are used to model this phenomenon in a nonlinear way, which allows for a nonmonotonic degradation path. In many situations, the relative efficiency of the lifetime estimate is improved over the standard estimators based on transformed linear models.

/pdf/a-nonlinear-random-coefficients-model-for-degradation-2tuwe7i0j7.pdf

A Nonlinear Random-Coefficients Model for Degradation Testing

Light-emitting diodes (LEDs) are among the key innovations that have revolutionized the lighting industry, due to their versatility in applications, higher reliability, longer lifetime, and higher efficiency compared with other light sources. The demand for increased lifetime and higher reliability has attracted a significant number of research studies on the prognostics and lifetime estimation of LEDs, ranging from the traditional failure data analysis to the latest degradation modeling and machine learning based approaches over the past couple of years. However, there is a lack of reviews that systematically address the currently evolving machine learning algorithms and methods for fault detection, diagnostics, and lifetime prediction of LEDs. To address those deficiencies, a review on the diagnostic and prognostic methods and algorithms based on machine learning that helps to improve system performance, reliability, and lifetime assessment of LEDs is provided. The fundamental principles, pros and cons of methods including artificial neural networks, principal component analysis, hidden Markov models, support vector machines, and Bayesian networks are presented. Finally, discussion on the prospects of the machine learning implementation from LED packages, components to system level reliability analysis, potential challenges and opportunities, and the future digital twin technology for LEDs lifetime analysis is provided.

/pdf/machine-learning-and-digital-twin-driven-diagnostics-and-58oro206ul.pdf

Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light-Emitting Diodes

Accelerated life testing (ALT) is commonly used to obtain reliability information about a product in a timely manner. Several stress loading designs have been proposed and recent research interests have emerged concerning the development of equivalent ALT plans. Step-stress ALT (SSALT) is one of the most commonly used stress loadings because it usually shortens the test duration and reduces the number of required test units. This article considers two fundamental questions when designing a SSALT and provides formal proofs in answer to each. Namely: (1) can a simple SSALT be designed so that it is equivalent to other stress loading designs? (2) when optimizing a multilevel SSALT, does it degenerate to a simple SSALT plan? The answers to both queries, under certain reasonable model assumptions, are shown to be a qualified YES. In addition, we provide an argument to support the rationale of a common practice in designing a SSALT, that is, setting the higher stress level as high as possible in a SSALT plan. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2013

Statistical equivalency and optimality of simple step‐stress accelerated test plans for the exponential distribution

Using data from a simple step-stress accelerated life test procedure, a non-parametric proportional hazards model is proposed for obtaining upper confidence bounds for the cumulative failure probability of a product under normal use conditions The approach is non-parametric in the sense that most of the functions involved in the model do not assume any specific forms, except for certain verifiable conditions Test statistics are introduced to verify assumptions about the model and to test the goodness of fit of the proposed model to the data A numerical example, using data simulated from the lifetime distribution of an existing parametric study on metal-oxide semiconductor capacitors, is used to illustrate the proposed methods Discussions on how to determine the optimal stress levels and sample size are also given

https://webapps.krannert.purdue.edu/kap/KATALYST/public/publications.aspx?personid=20247&publicationid=7122&attachmentid=608

Step-stress accelerated life tests: A proportional hazards-based non-parametric model

6 1 There are so many systems that rely on a continuous source of power that even minor interruptions of service can result in major and costly losses. Hospitals, traffic lights, computers that run financial systems, air traffic controls, nuclear power plants, military sites, water and sewage plants, and telephone and internet communications are all vulnerable. Loss of service is inevitable, especially from acts of nature (storms, wind, lightning, etc.), accidents such as a car hitting a telephone pole and knocking down transmission lines, failure of equipment, or intentional attacks meant to cause widespread outages and cause as much damage as possible. However, today’s system is designed to mitigate and isolate the damaged section. The author proposes, in seven short chapters, methods to change the existing system to one that is more secure and less likely to be able to cause a cascading blackout. Some of these are simple proposals, such as making substations more secure by adding barbed wire. Others are more involved such as sectionalizing the grid to eliminate massive blackouts. The focus of this book is primarily on transmission line security, as it is very exposed and difficult to protect. Chapters include proposed circuit configurations, electrical and mechanical criteria, substations, DC transmission, operations, and economics. Some of the proposed circuit configurations and descriptions are interesting. However, many of the drawings lack any explanation, and the text does not describe the diagrams in sufficient detail. Also, there are references to outdated material (e.g., references to Westinghouse Electric). Although interesting, most of the proposals lack any technical details, implementation, and costs. Appendices follow the main body (over two-thirds of the book) and include the pros and cons of polymer versus porcelain insulators and a study on the national transmission grid (233 pages). The study provides details for more capacity, planning issues, and advanced transmission technologies. This book would be useful for the technical or non-technical reader interested in learning more about how transmission lines are configured today, before and after deregulation, and to learn about the weaknesses in today’s transmission system. Simulation Methods for Polymers Edited by M. Kotelyanskii and D.N. Theodorou Marcel Dekker, Inc. 270 Madison Ave. New York, NY 10016 ISBN# 0-8247-0247-6 Phone: +800 228 1160 Fax: +212 685 4540 http://www.dekker.com 602 pp. -$195.00 (Hardcover), 2004

J.J. Shea

Papers

Accelerated Testing- Statistical Models, Test Plans, and Data Analysis [Book Review]