--- format: "aidr-story-markdown/v1" id: "7b350c4dafe600daf645a71902ce69d1b56ba73d27336271df12e4d34cf53f92" canonical_url: "https://aidr.today/7b350c4d?lang=en" title: "Neutrosophic Ensemble Classification for Uncertainty-Aware Bearing Fault Detection: Evidence from Laboratory and Variable-Speed Industrial Benchmarks" lang: "en" requested_lang: "en" available_langs: ["en","vi"] translation_fallback: null fallback_fields: [] published_at: "2026-10-07T04:00:00.000Z" category: "Research" topics: ["inference"] source_urls: ["https://arxiv.org/abs/2610.06880"] summary: "A neutrosophic ensemble combining Random Forest, XGBoost, and Logistic Regression decomposes predictions into four indicators for uncertainty-aware bearing fault detection, evaluated on CWRU and JNU benchmarks under a leave-one-condition-out protocol. On CWRU, the ensemble reaches 100.00 percent accuracy on three of four held-out loads (92.27 percent on the fourth), while on JNU, accuracy drops to 40.64 percent, below a majority-class baseline, with Logistic Regression (57.91 percent) generalizing better than tree ensembles." --- # Neutrosophic Ensemble Classification for Uncertainty\-Aware Bearing Fault Detection: Evidence from Laboratory and Variable\-Speed Industrial Benchmarks > [Open the canonical story]() **Published:** 2026-10-07T04:00:00.000Z **Category:** Research **Topics:** inference ## Summary A neutrosophic ensemble combining Random Forest, XGBoost, and Logistic Regression decomposes predictions into four indicators for uncertainty\-aware bearing fault detection, evaluated on CWRU and JNU benchmarks under a leave\-one\-condition\-out protocol\. On CWRU, the ensemble reaches 100\.00 percent accuracy on three of four held\-out loads \(92\.27 percent on the fourth\), while on JNU, accuracy drops to 40\.64 percent, below a majority\-class baseline, with Logistic Regression \(57\.91 percent\) generalizing better than tree ensembles\. ## Sources - [Story source]()