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    Machine learning-based Shapley additive explanations approach for corroded pipeline failure mode identification
    (Elsevier Ltd, 2024) Ben Seghier, Mohamed El Amine; Mohamed, Osama Ahmed; Ouaer, Hocine
    Rapid failure mode identification of oil and gas pipelines can prevent catastrophic consequences, improve fast intervention and enhance the design safety of these critical systems. This paper proposes explainable-based machine learning models using to determine the failure mode of corroded pipelines as a function of geometric configurations, material properties, and corrosion defect details. To determine the best identification model, this study examined eight machine learning models, including Nave Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and Category Boosting, based on a comprehensive experimental database for steel pipelines with various corrosion/crack defect configurations. Furthermore, the Shapley additive explanations approach is utilized to rank the input variables for failure mode identification and explains the machine learning model predicting a specific failure mode for a given sample. In identifying the failure mode of corroded pipelines, the proposed Extreme Gradient Boosting model indicated the highest accuracy in term of performance evaluation compared to all other proposed models. In addition, the model-explanation findings show that the important parameter influencing the failure mechanism of corroded pipelines is the depth of corrosion defects followed by the pipeline wall thickness. The proposed framework is adaptable enough to allow further use of experimental results for having new insights.
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    Comportament flexibil forfecare a grinzilor de beton armat Întărit de diverse rețele de composite încorporate
    (2020) Aouadi, Abdelhak; Ghernouti, Youcef
    This paper evaluate the flexural–shear behaviour of reinforced concrete beams strengthening by embedded carbon fiber reinforced polymer grids (CFRP grids) and metallic grids with hexagonal meshes, under four-point bending test. An experimental investigation was carried out to evaluate the performance of this new technical of strengthening, Three control beams and fifteen beams strengthened in flexural–shear with different configurations have been tested. Several parameters were considered to highlight the efficiency of the technical used, such as; ultimate load, ductility and failure modes. The experimental results show that the beams strengthening by embedded carbon and metallic grids offered a great improvement in strength, midspan deflection and ductility index; in effect, an influence directly on the failure mode is observed
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    Dependability analysis in systems engineering approach using the FMECA extracted from the SysML and failure modes classification by K-means
    (Springer, 2021) Chabane, Ali; Adjerid, Smail; Meddour, Ikhlas
    The work presented in this article is a contribution to the implementation of an approach dedicated to the behavioral analysis of industrial systems starting from the design Maintenance engineering techniques inspire the suggested approach, and it aims at deducing and classifying the industrial systems' probable failure modes. The latter modes can alter the systems proper functioning. Our suggested approach is a combination of three complementary tools. The SysML language is applied to express customers' needs and requirements, such as future systems' functions and operating conditions. Besides, the FMECA method analyzes systems' potential dysfunction and the recommendation of appropriate maintenance actions. Finally, the K-means method classifies failure modes to get detailed mode criticality instead of calculating this latter according to ancient methods. The result will objectively make it possible to develop systems with reliable and maintainable components. It also helps to recommend optimal maintenance strategies according to the equipment evolution state. The approach is carried out through two application cases. The first is a practical and straightforward system used to check the methods feasibility, and the second is a more elaborated one, used to observe the effectiveness of the approach
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    Reliability assessment and improvement of medium power induction motor winding insulation protection system using predictive analysis
    (Springer, 2009) Chafai, M.; Refoufi, L.; Bentarzi, Hamid
    This chapter presents a reliability assessment of a widely used protection system of medium-power squirrel cage induction motors. In conjunction with published field induction motors reliability data, this assessment effort is based on a predictive analysis integrating three predictive techniques: (1) a fault tree analysis (FTA) that allows to identify and then quantify the initiating events weighting factors; (2) an event tree analysis (ETA) that allows to predict the protection system probability outcomes following an external disturbance; and (3) a failure mode effect and criticality analysis (FMECA) that will help set the stage to develop a preventive maintenance program fit to keep up the induction motor protection system reliability at the required level with particular attention given to aggressive environmental factors such as found in cement plants