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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Related Experiment Video

Updated: Jul 8, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Survival Models for Predictive Maintenance and Remaining Useful Life in Sensor-Enabled Smart Energy Networks: A

Mohammad Reza Shadi1, Hamid Mirshekali1, Maryamsadat Tahavori2

  • 1SDU Center for Energy Informatics, Maersk Mc-Kinney Moeller Institute, The Faculty of Engineering, University of Southern Denmark, 5230 Odense, Denmark.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

This review covers survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation in smart energy networks. It emphasizes accounting for incomplete time-to-event data to ensure accurate maintenance planning.

Keywords:
Cox proportional hazardsRUL predictionasset managementcensoring and truncationlearning-based survival modelsrandom survival forest

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Last Updated: Jul 8, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Engineering
  • Data Science
  • Energy Systems

Background:

  • Smart energy networks require maintenance under complex, dynamic conditions.
  • Time-to-event data in these systems are often incomplete due to censoring and truncation.
  • Accurate maintenance decisions depend on models that handle partially observed lifetimes.

Purpose of the Study:

  • To review survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation.
  • To emphasize censoring-aware formulations and the use of diverse data sources.
  • To provide a guide for selecting, fitting, and evaluating these models for smart energy networks.

Main Methods:

  • Survey of non-parametric, semi-parametric, parametric, and learning-based survival models.
  • Focus on models incorporating static and time-varying covariates from sensor, inspection, and contextual data.
  • Systematic mapping of model families to data types, assumptions, and outputs.

Main Results:

  • A structured taxonomy of survival models for PdM and RUL is presented.
  • Models are mapped to data types, assumptions (e.g., proportional hazards), and outputs (e.g., risk ranking, RUL distributions).
  • Evaluation practices, including discrimination metrics and censoring-aware accuracy measures, are synthesized.

Conclusions:

  • Survival models are crucial for risk-informed maintenance planning in smart energy networks.
  • Accounting for data censoring and truncation is essential for unbiased inference and reliable performance estimates.
  • This review offers a practical framework for researchers and practitioners in selecting and applying appropriate survival models.