Affiliation:
1. Statistical Sciences Group Los Alamos National Laboratory Los Alamos New Mexico USA
2. Weapons Directorate Los Alamos National Laboratory Los Alamos New Mexico USA
3. Department of Statistics Brigham Young University Provo Utah USA
Abstract
AbstractDespite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated‐stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). This review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high‐stress test‐analyze‐fix‐test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.This article is categorized under:
Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control