Probabilistic models of fatigue crack growth: Results and speculations

Probabilistic Engineering Mechanics

Baraldi

Turati

et al. 2015

Particle Filtering (PF) is a model-based, filtering technique, which has drawn the attention of the Prognostic and Health Management (PHM) community due to its applicability to nonlinear models with non-additive and non-Gaussian noise. When multiple physical models can describe the evolution of the degradation of a component, the PF approach can be based on Multiple Swarms (MS) of particles, each one evolving according to a different model, from which to select the most accurate a posteriori distribution. However, MS are highly computational demanding due to the large number of particles to simulate. In this work, to tackle the problem we have developed a PF approach based on the introduction of an augmented discrete state identifying the physical model describing the component evolution, which allows to detect the occurrence of abnormal conditions and identifying the degradation mechanism causing it. A crack growth degradation problem has been considered to prove the effectiveness of the proposed method in the detection of the crack initiation and the identification of the occurring degradation mechanism. The comparison of the obtained results with that of a literature MS method and of an empirical statistical test has shown that the proposed method provides both an early detection of the crack initiation, and an accurate and early identification of the degradation mechanism. A reduction of the computational cost is also achieved.2

Section: Model Descriptionmentioning

confidence: 99%

Interacting multiple-models, state augmented Particle Filtering for fault diagnostics

Probabilistic Engineering Mechanics

Baraldi

Turati

et al. 2015

“…The justification of this choice is twofold: on one side, such a model has been shown to suitably approximate the degradation evolution of mechanical components subject to fatigue loads [21]; on the other side, it has been successfully applied in applications to prognostics (e.g., [7]). …”

Section: A Degradation and Measurement Modelsmentioning

confidence: 99%

“…Several models have been developed in the literature to describe the evolution of this complex physical phenomenon (e.g., gamma processes [19], Weibull-based discrete processes [20], etc.). In this work, we adopt the randomized Paris-Erdogan fatigue crack growth model, which is a popular approach in the literature (e.g., [21]). …”

Section: A Degradation and Measurement Modelsmentioning

confidence: 99%

Predictive Maintenance by Risk Sensitive Particle Filtering

Zio

2014

IEEE Trans. Rel.

Predictive Maintenance (PrM) exploits the estimation of the equipment Residual Useful Life (RUL) to identify the optimal time for carrying out the next maintenance action. Particle Filtering (PF) is widely used as a prognostic tool in support of PrM, by reason of its capability of robustly estimating the equipment RUL without requiring strict modeling hypotheses. However, a precise PF estimate of the RUL requires tracing a large number of particles, and thus large computational times, often incompatible with the need of rapidly processing information for making maintenance decisions in due time. This work considers two different Risk Sensitive Particle Filtering (RSPF) schemes proposed in the literature, and investigates their potential for PrM. The computational burden problem of PF is addressed. The effectiveness of the two algorithms is analyzed on a case study concerning a mechanical component affected by fatigue degradation.Index Terms-Predictive maintenance, risk function, risk sensitive particle filtering.

“…In this work, we adopt the randomized Paris -Erdogan fatigue crack growth model, a popular approach propounded in the literature (e.g., Provan 1987, Kozin andBogdanoff 1989). The justification of this choice is threefold: on one side, such model has been shown to suitably approximate the degradation evolution of mechanical components subject to fatigue loads (Provan 1987, Kozin andBogdanoff 1989); on another side, it has been successfully applied in applications to prognostics (e.g., Cadini et al 2009); finally, the precise modeling of the crack growth process is not the central focus of the present work and, in this view, the simplicity of the model adopted is appealing for the purposes of the paper. Its most widely used expressions is:…”

Section: Reference Examplementioning

confidence: 99%

Evaluating maintenance policies by quantitative modeling and analysis

Zio

Reliability Engineering & System Safety

2013

110

To cite this version:Enrico Zio, Michele Compare.Evaluating maintenance policies by quantitative modeling and analysis.Reliability Abstract: The growing importance of maintenance in the evolving industrial scenario and the technological advancements of the recent years have yielded the development of modern maintenance strategies such as the Condition-Based Maintenance (CBM) and the Predictive Maintenance (PrM). In practice, assessing whether these strategies really improve the maintenance performance becomes a fundamental issue. In the present work, this is addressed with reference to an example concerning the stochastic crack growth of a generic mechanical component subject to fatigue degradation. It is shown that modeling and analysis provide information useful for setting a maintenance policy.