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From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations JFinTEB: Japanese Financial Text Embedding Benchmark UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts Collaborative Filtering Through Weighted Similarities of User and Item Embeddings IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model Evaluation of Agents under Simulated AI Marketplace Dynamics
Understanding the main failure scenarios of subsea blowou...
Gustavo Jorge Martins de Aguiar, Ramon Baptista Narcizo, Rodolfo · 2023-01-03 · via cs.IR updates on arXiv.org

The blowout preventer (BOP) system is one of the most important well safety barriers during the drilling phase because it can prevent the development of blowout events. This paper investigates BOP system's main failures using an LSA-based methodology. A total of 1312 failure records from companies worldwide were collected from the International Association of Drilling Contractors' RAPID-S53 database. The database contains recordings of halted drilling operations due to BOP system's failures and component's function deviations. The main failure scenarios of the components annular preventer, shear rams preventer, compensated chamber solenoid valve, and hydraulic regulators were identified using the proposed methodology. The scenarios contained valuable information about corrective maintenance procedures, such as frequently observed failure modes, detection methods used, suspected causes, and corrective actions. The findings highlighted that the major failures of the components under consideration were leakages caused by damaged elastomeric seals. The majority of the failures were detected during function and pressure tests with the BOP system in the rig. This study provides an alternative safety analysis that contributes to understanding blowout preventer system's critical component failures by applying a methodology based on a well-established text mining technique and analyzing failure records from an international database.