Reliability engineering and risk analysis : a practical guide

By Mohammad Modarres, M. Kaminskiy, Vasiliy V. Krivtsov. 2016

362 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d7d27ed5ad148315a9777b093be00887d8a516b1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ping Wang
2. Chandan K. Reddy
3. Yan Li
4. Lev V. Utkin
5. Yu Zhang

Citations in the corpus

• Machine Learning for Survival Analysis: A Survey (2017, methodology): “days of discharge. Demographics: age, gender, race. Measurements: height, weight, disease history, disease type, treatment, comorbidities, laboratory, procedures, medications. Reliability [Lyu 1996] [Modarres et al. 2009] Device failure Likelihood of a device being failed within tdays. Product: mode”
• Machine Learning for Survival Analysis (2019, methodology): “Reliability prediction focuses on developing methods which are good at accurately estimating the reliability of new products (Modarres et al. 2009; Lyu 1996).”
• Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy (2020, unknown): “Reliable (Modarres et al., 2016) systems come from appropriate technical practices, which support human responsibility (Canadian Government, 2019), fairness (O’Neil, 2016), and explainability (Du et al.”