164 research outputs found
Degradation modeling applied to residual lifetime prediction using functional data analysis
Sensor-based degradation signals measure the accumulation of damage of an
engineering system using sensor technology. Degradation signals can be used to
estimate, for example, the distribution of the remaining life of partially
degraded systems and/or their components. In this paper we present a
nonparametric degradation modeling framework for making inference on the
evolution of degradation signals that are observed sparsely or over short
intervals of times. Furthermore, an empirical Bayes approach is used to update
the stochastic parameters of the degradation model in real-time using training
degradation signals for online monitoring of components operating in the field.
The primary application of this Bayesian framework is updating the residual
lifetime up to a degradation threshold of partially degraded components. We
validate our degradation modeling approach using a real-world crack growth data
set as well as a case study of simulated degradation signals.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS448 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Adaptive Two-stage Stochastic Programming with an Application to Capacity Expansion Planning
Multi-stage stochastic programming is a well-established framework for
sequential decision making under uncertainty by seeking policies that are fully
adapted to the uncertainty. Often such flexible policies are not desirable, and
the decision maker may need to commit to a set of actions for a number of
planning periods. Two-stage stochastic programming might be better suited to
such settings, where the decisions for all periods are made here-and-now and do
not adapt to the uncertainty realized. In this paper, we propose a novel
alternative approach, where the stages are not predetermined but part of the
optimization problem. Each component of the decision policy has an associated
revision point, a period prior to which the decision is predetermined and after
which it is revised to adjust to the uncertainty realized thus far. We motivate
this setting using the multi-period newsvendor problem by deriving an optimal
adaptive policy. We label the proposed approach as adaptive two-stage
stochastic programming and provide a generic mixed-integer programming
formulation for finite stochastic processes. We show that adaptive two-stage
stochastic programming is NP-hard in general. Next, we derive bounds on the
value of adaptive two-stage programming in comparison to the two-stage and
multi-stage approaches for a specific problem structure inspired by the
capacity expansion planning problem. Since directly solving the mixed-integer
linear program associated with the adaptive two-stage approach might be very
costly for large instances, we propose several heuristic solution algorithms
based on the bound analysis. We provide approximation guarantees for these
heuristics. Finally, we present an extensive computational study on an
electricity generation capacity expansion planning problem and demonstrate the
computational and practical impacts of the proposed approach from various
perspectives
Predicting Remaining Useful Life with Similarity-Based Priors
Prognostics is the area of research that is concerned with predicting the remaining useful life of machines and machine parts. The remaining useful life is the time during which a machine or part can be used, before it must be replaced or repaired. To create accurate predictions, predictive techniques must take external data into account on the operating conditions of the part and events that occurred during its lifetime. However, such data is often not available. Similarity-based techniques can help in such cases. They are based on the hypothesis that if a curve developed similarly to other curves up to a point, it will probably continue to do so. This paper presents a novel technique for similarity-based remaining useful life prediction. In particular, it combines Bayesian updating with priors that are based on similarity estimation. The paper shows that this technique outperforms other techniques on long-term predictions by a large margin, although other techniques still perform better on short-term predictions.</p
Residual Life Distributions from Component Degradation Signals: A Bayesian Approach
Received and accepted Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can the
A degradation-based model for joint optimization of burn-in, quality inspection, and maintenance: a light display device application
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