2026-10-10T17:08:13Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33235530 2026-09-18T11:47:20Z category_26704 item_type_3 month_year_09_2026

Reproducibility package for “From prediction to explanation: An ablation and verification framework for LLM explanations of marine diesel engine fault diagnosis” Abdul Hafiz Al Hariri (24560652) Ship and platform structures (incl. maritime hydrodynamics) Explainable AI Fault Diagnosis Large language models SHAP-Retrieval-augmented generation Explanation faithfulness Neural-network fault diagnosis <p dir="ltr">Reproducibility Package<b>:</b> <i>Beyond the Fault Label: Auditable SHAP-RAG-LLM Explanations for Marine Diesel Health Monitoring</i></p><p dir="ltr"><br></p><p dir="ltr">This archive preserves the machine-readable artifacts used to audit the paper’s ANN, SHAP, prompt, LLM, and safety-sensitivity results for five marine-diesel states and two exhaust-gas-temperature (EGT) representations. The primary benchmark uses two models, two feature profiles, five evidence modes (M0-M4), and 25 balanced cases per group: <b>2 x 2 x 5 x 25 = 500 outputs</b>. The main analysis uses <b>top-k = 5</b>; top-k = 3 and 7 are sensitivity checks.</p><p dir="ltr">This is an <b>audit/results package</b>, not an end-to-end training repository. It supports exact inspection and recalculation from saved predictions, attributions, prompts, responses, and threshold tables. The original ERS-500 workbooks, executable source code, model weights, API credentials, and expert-review scoring sheets are not included; therefore, data regeneration, model retraining, and new LLM calls require those external materials.</p> 2026-09-18T11:47:20Z Dataset Dataset 10.6084/m9.figshare.33235530.v1 https://figshare.com/articles/dataset/Reproducibility_package_for_From_prediction_to_explanation_An_ablation_and_verification_framework_for_LLM_explanations_of_marine_diesel_engine_fault_diagnosis_/33235530 CC BY 4.0