Neutrosophic Ensemble Classification for Uncertainty-Aware Bearing Fault Detection: Insights from Laboratory and Industr

Summary:This research introduces a Random Forest, XGBoost, and Logistic Regression ensemble method based on Neutrosophic Theory for bearing fault detection. The approach decomposes model outputs into four key indicators: top-class evidence (T-hat), best-competitor evidence (F-hat), predictive entropy (I1-hat), and decision disagreement (I2-hat). Evaluated on the CWRU and JNU bearing benchmarks, the method demonstrates strong performance in handling uncertainty, achieving near-perfect classification accu

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Background and Motivation

Bearing fault detection is a critical component of industrial equipment maintenance. Traditional machine learning classifiers generate scalar confidence scores, which often fail to distinguish between genuine uncertainty and confident errors. This research aims to address this limitation by leveraging Neutrosophic Theory to provide a more nuanced framework for uncertainty analysis.

Methodology and Implementation

1. Model Ensemble: Utilizes an ensemble of Random Forest, XGBoost, and Logistic Regression.
2. Neutrosophic Decomposition: Decomposes model outputs into four indicators:
   • T-hat: Top-class evidence
   • F-hat: Best-competitor evidence
   • I1-hat: Predictive entropy
   • I2-hat: Decision disagreement
3. Experimental Setup: Evaluated on the CWRU and JNU bearing benchmarks using a leave-one-condition-out protocol.

Key Findings

• CWRU Benchmark: After correcting a file-to-class mapping error, the ensemble method achieved 100% accuracy on three out of four held-out loads, with 92.27% on the fourth.
• JNU Benchmark: Excluding the 1000 rpm condition, the accuracy dropped to 40.64%, below the majority-class baseline. Logistic Regression (57.91%) outperformed the tree ensembles.
• Uncertainty Analysis: I1-hat showed a robust association with error beyond T-hat/F-hat, while I2-hat contributed little. Standalone Logistic Regression confidence outperformed the full decomposition.
• Additional Results: Fusion of time-domain and frequency-domain models and envelope spectrum-based fault frequency demodulation showed promise in specific scenarios.

Conclusion and Outlook

This study demonstrates the potential of Neutrosophic Theory in bearing fault detection, particularly in handling uncertainty. However, the varying performance of different methods across scenarios suggests that future research should focus on the applicability and adaptability of methods. Additionally, the combination of model fusion and signal processing techniques offers new avenues for enhancing fault detection performance.

Recommendations for Developers

• Model Selection: When dealing with complex industrial data, consider combining multiple models and methods to enhance robustness.
• Uncertainty Analysis: Emphasize the application of uncertainty indicators, especially in high-risk scenarios.
• Signal Processing Techniques: Combining advanced signal processing techniques (e.g., envelope spectrum analysis) can significantly improve fault detection performance.

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Source: ArXiv Machine Learning (cs.LG) (2026-10-07)

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Tags: #Machine Learning Follow #Fault Detection Follow #Neutrosophic Theory Follow #Uncertainty Analysis Follow #Industrial AI Follow