Evaluating maintenance policies by quantitative modeling and analysis

International audienceThe 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 funda-mental 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

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

International audienceParticle 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.

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Link to publishers version: 10.1016/j.ress.2016.03.004We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating condition

Aggregation of importance measures for decision making in reliability engineering

This article investigates the aggregation of rankings based on component importance measures to provide the decision maker with a guidance for design or maintenance decisions. In particular, ranking aggregation algorithms of the literature are considered, a procedure for ensuring that the aggregated ranking is compliant with the Condorcet criterion of majority principle is presented and two original ranking aggregation approaches are proposed. Comparisons are made on a case study of an auxiliary feed-water system of a nuclear pressurized water reactor

Improving scheduled maintenance by missing data reconstruction: A double-loop Monte Carlo approach

This article describes a Monte Carlo-based approach for reconstructing missing information in a dataset used by General Electric for reliability analysis, which contains data coming from field observations at inspection of gas turbine components. The approach is based on a combination of maximum likelihood estimation technique to estimate the failure model parameters, Fisher information matrix to estimate the confidence intervals on the estimated parameters, and a double-loop Monte Carlo approach to estimate the missing equivalent starts (i.e. data of turbine state without the relative equivalent starts). The proposed methodology reduces the uncertainty in the estimation of the parameters of the turbine. The results of the application of the novel approach to a real industrial dataset are discussed along with a sensitivity analysis for the quantification of the robustness of the methodology to deal with different sizes of datasets

Availability Model of a PHM-Equipped Component

A variety of prognostic and health management (PHM) algorithms have been developed in the last years and some metrics have been proposed to evaluate their performances. However, a general framework that allows us to quantify the benefit of PHM depending on thesemetrics is still lacking.We propose a general, time-variant, analytical model that conservatively evaluates the increase in system availability achievable when a component is equipped with a PHM system of known performance metrics. The availability model builds on metrics of literature and is applicable to different contexts. A simulated case study is presented concerning crack propagation in a mechanical component. A simplified costmodel is used to compare the performance of predictive maintenance based on PHM with corrective and scheduled maintenance

Reliability model of a component equipped with PHM capabilities

We propose an analytic, time-variant model that conservatively evaluates the increase in reliability achievable when a component is equipped with a Prognostics and Health Management system of known performance metrics. The reliability model builds on metrics of literature and is applicable to different industrial contexts. A simulated case study concerning crack propagation in a mechanical component is considered to validate the proposed model

A clustering approach for mining reliability big data for asset management

Big data from very large fleets of assets challenge the asset management, as the number of maintenance strategies to optimize and administrate may become very large. To address this issue, we exploit a clustering approach that identifies a small number of sets of assets with similar reliability behaviors. This enables addressing the maintenance strategy optimization issue once for all the assets belonging to the same cluster and, thus, introduces a strong simplification in the asset management. However, the clustering approach may lead to additional maintenance costs, due to the loss of refinement in the cluster reliability model. For this, we propose a cost model to support asset managers in trading off the simplification brought by the cluster-based approach against the related extra costs. The proposed approach is applied to a real case study concerning a set of more than 30,000 switch point machines

Protective effects of Lactobacillus paracasei F19 in a rat model of oxidative and metabolic hepatic injury

The liver is susceptible to such oxidative and metabolic stresses as ischemia-reperfusion (I/R) and fatty acid accumulation. Probiotics are viable microorganisms that restore the gut microbiota and exert a beneficial effect on the liver by inhibiting bacterial enzymes, stimulating immunity, and protecting intestinal permeability. We evaluated Lactobacillus paracasei F19 (LP-F19), for its potential protective effect, in an experimental model of I/R (30 min ischemia and 60 min reperfusion) in rats fed a standard diet or a steatogen [methionine/choline-deficient (MCD)] diet. Both groups consisted of 7 sham-operated rats, 10 rats that underwent I/R, and 10 that underwent I/R plus 8 wk of probiotic dietary supplementation. In rats fed a standard diet, I/R induced a decrease in sinusoid perfusion (P < 0.001), severe liver inflammation, and necrosis besides an increase of tissue levels of malondialdehyde (P < 0.001), tumor necrosis factor-alpha (P < 0.001), interleukin (IL)-1beta (P < 0.001), and IL-6 (P < 0.001) and of serum levels of transaminase (P < 0.001) and lipopolysaccharides (P < 0.001) vs. sham-operated rats. I/R also induced a decrease in Bacterioides, Bifidobacterium, and Lactobacillus spps (P < 0.01, P < 0.001, and P < 0.001, respectively) and an increase in Enterococcus and Enterobacteriaceae (P < 0.01 and P < 0.001, respectively) on intestinal mucosa. The severity of liver and gut microbiota alterations induced by I/R was even greater in rats with liver inflammation and steatosis, i.e., MCD-fed animals. LP-F19 supplementation significantly reduced the harmful effects of I/R on the liver and on gut microbiota in both groups of rats, although the effect was slightly less in MCD-fed animals. In conclusion, LP-F19 supplementation, by restoring gut microbiota, attenuated I/R-related liver injury, particularly in the absence of steatosis