Statistical Methods for Creep, Fatigue and Fracture Data Analysis

1979 ◽  
Vol 101 (4) ◽  
pp. 344-348 ◽  
Author(s):  
G. J. Hahn
Keyword(s):  
Statistical Methods ◽  
Failure Time ◽  
High Stress ◽  
Problem Definition ◽  
Time Data ◽  
Design Concepts ◽  
Fracture Data ◽  
Fatigue And Fracture ◽  
Creep Fatigue

Statistical methods can play an important role in the proper collection and analysis of creep, fatigue and fracture data. This article deals with what statistics has to offer in this area. The following subjects are considered: The role of statistics in problem definition; the use of stress versus time as the dependent variable in the data fitting; treatment of variability between heats; analysis of data with run-outs; exclusion of extreme high stress-low failure time data from the analysis; experimental design concepts for obtaining the most useful data. The article is based upon a paper prepared at Bob Goldhoff’s suggestion and with his substantial assistance. This paper was originally presented to a 1977 NASA/EPRI/MPC Workshop.

scholarly journals Improving Stress Management and Sleep Hygiene in Intelligent Homes

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2398
Author(s):  
Asterios Leonidis ◽  
Maria Korozi ◽  
Eirini Sykianaki ◽  
Eleni Tsolakou ◽  
Vasilios Kouroumalis ◽  
...  
Keyword(s):  
Sleep Disturbances ◽  
Contextual Information ◽  
High Stress ◽  
Sleep Hygiene ◽  
Poor Sleep ◽  
Health Issues ◽  
Time Data ◽  
Intelligent Home ◽  
And Control

High stress levels and sleep deprivation may cause several mental or physical health issues, such as depression, impaired memory, decreased motivation, obesity, etc. The COVID-19 pandemic has produced unprecedented changes in our lives, generating significant stress, and worries about health, social isolation, employment, and finances. To this end, nowadays more than ever, it is crucial to deliver solutions that can help people to manage and control their stress, as well as to reduce sleep disturbances, so as to improve their health and overall quality of life. Technology, and in particular Ambient Intelligence Environments, can help towards that direction, when considering that they are able to understand the needs of their users, identify their behavior, learn their preferences, and act and react in their interest. This work presents two systems that have been designed and developed in the context of an Intelligent Home, namely CaLmi and HypnOS, which aim to assist users that struggle with stress and poor sleep quality, respectively. Both of the systems rely on real-time data collected by wearable devices, as well as contextual information retrieved from the ambient facilities of the Intelligent Home, so as to offer appropriate pervasive relaxation programs (CaLmi) or provide personalized insights regarding sleep hygiene (HypnOS) to the residents. This article will describe the design process that was followed, the functionality of both systems, the results of the user studies that were conducted for the evaluation of their end-user applications, and a discussion about future plans.

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

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