Uncertainty NVAE-SVDD Based on Monte Carlo Dropout for Trustworthy Detection of Mechanical Failures
Zhiyi He, Qing Liu, Haidong Shao, Bin Yang, Ming Zeng
Abstract
Most mechanical fault detection methods rely on deterministic feature extraction and fail to model prediction uncertainty, often leading to overconfident errors when facing unknown or borderline faults in complex conditions. This compromises the reliability of detection outcomes. To address these issues, this article proposes a trustworthy mechanical fault detection method based on uncertainty modeling using Monte Carlo (MC) dropout within the nouveau variational autoencoder support vector data description (NVAE-SVDD) framework. The method employs a NVAE with a hierarchical latent variable structure as the feature extractor. By explicitly incorporating the MC dropout strategy, it enables effective quantification of uncertainty during the prediction process. Meanwhile, SVDD is used to construct a compact boundary around normal samples in the NVAE latent space, enabling efficient fault identification. The proposed method not only accurately identifies normal conditions but also fully leverages predictive uncertainty to distinguish boundary anomalies and unknown faults. Experimental results show that the proposed method outperforms traditional one-class models and uncertainty-free baselines in accuracy and robustness, greatly improving fault detection reliability.