Some life tests result in few or no failures. In such cases, it is difficult to assess reliability with traditional life tests that record only time to failure. For some devices, it is possible to obtain degradation measurements over time, and these measurements may contain useful information about product reliability. Even with little or no censoring, there may be important practical advantages to analyzing degradation data. If failure is defined in terms of a specified level of degradation, a degradation model defines a particular time-to-failure distribution. Generally it is not possible to obtain a closed-form expression for this distribution. The purpose of this work is to develop statistical methods for using degradation measures to estimate a time-to-failure distribution for a broad class of degradation models. We use a nonlinear mixed-effects model and develop methods based on Monte Carlo simulation to obtain point estimates and confidence intervals for reliability assessment.

Using Degradation Measures to Estimate a Time-to-Failure Distribution

A condition-based maintenance policy for stochastically deteriorating systems

State-of-the-art report: Fracture mechanics of concrete: concepts, models and determination of material properties. Why fracture mechanics? Essential results from linear elastic fracture mechanics. Nonlinear fracture models with softening zone. Special nonlinear fracture models based on adaptions of LEFM. Size effect and brittleness of structures. Experimental or analytical determination of material fracture parameters. Factors influencing fracture parameters. Effect of reinforcement. Crack systems. Concluding remarks. References and bibliography. Appendix 1- derivations of some formulas. Conference papers. Material models for concrete fracture. Damage modelling. Numerical analysis of concrete fracture. Experimental methods and determination of fracture characteristics. Measurements of damage and size effect. Dynamic fracture. Fracture under shear. Fracture of reinforced concrete. Interaction between concrete and reinforcement. Fatigue and rate effects. Environment effects [temperature, shrinkage, corrosion]. Index.

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Fracture mechanics of concrete structures

Prognostics and health management (PHM) is a framework that offers comprehensive yet individualized solutions for managing system health. In recent years, PHM has emerged as an essential approach for achieving competitive advantages in the global market by improving reliability, maintainability, safety, and affordability. Concepts and components in PHM have been developed separately in many areas such as mechanical engineering, electrical engineering, and statistical science, under varied names. In this paper, we provide a concise review of mainstream methods in major aspects of the PHM framework, including the updated research from both statistical science and engineering, with a focus on data-driven approaches. Real world examples have been provided to illustrate the implementation of PHM in practice.

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Prognostics and Health Management: A Review on Data Driven Approaches

The object of this investigation was to develop a procedure for making optimal maintenance decisions for a deteriorating system. The particular system chosen for study is a pavement, and a methodology is developed to ensure that pavements meet certain performance criteria while minimizing the expected maintenance cost. A cumulative damage model based upon a Markov process was developed to model pavement deterioration. The optimal repair action for each possible pavement state in the planning horizon was found by means of probabilistic dynamic programming. Sample sequences of repair actions were generated during a simulation in which the optimal repair policy was applied to sample pavement condition histories. Several sensitivity studies were performed to study the variation in expected cost, including the effect of delaying the optimal program.

J. L. Bogdanoff

Papers

Probabilistic Models of Cumulative Damage

A New Cumulative Damage Model: Part 1

A New Cumulative Damage Model—Part 4

A New Cumulative Damage Model: Part 2

A New Cumulative Damage Model—Part 3