Bi-directional digital twin prototype anchoring with multi-periodicity learning for few-shot fault diagnosis

2026-03-07

AI Summary

This work addresses the challenge of scarce labeled data in few-shot fault diagnosis for industrial equipment by proposing a digital twin–based meta-learning framework. A finite-element digital twin of an asynchronous motor is constructed to enable meta-training in a virtual space, while a test-time adaptation mechanism is introduced during physical testing. The approach features three key innovations: bidirectional prototype anchoring, covariance-guided data augmentation, and a multi-periodicity feature learning module, collectively enhancing model generalization under extremely limited fault samples. Extensive experiments across multiple few-shot settings and three operational conditions demonstrate the superiority of the proposed method, significantly outperforming existing digital twin–assisted diagnostic approaches.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Intelligent fault diagnosis (IFD) has emerged as a powerful paradigm for ensuring the safety and reliability of industrial machinery. However, traditional IFD methods rely heavily on abundant labeled data for training, which is often difficult to obtain in practical industrial environments. Constructing a digital twin (DT) of the physical asset to obtain simulation data has therefore become a promising alternative. Nevertheless, existing DT-assisted diagnosis methods mainly transfer diagnostic knowledge through domain adaptation techniques, which still require a considerable amount of unlabeled data from the target asset. To address the challenges in few-shot scenarios where only extremely limited samples are available, a bi-directional DT prototype anchoring method with multi-periodicity learning is proposed. Specifically, a framework involving meta-training in the DT virtual space and test-time adaptation in the physical space is constructed for reliable few-shot model adaptation for the target asset. A bi-directional twin-domain prototype anchoring strategy with covariance-guided augmentation for adaptation is further developed to improve the robustness of prototype estimation. In addition, a multi-periodicity feature learning module is designed to capture the intrinsic periodic characteristics within current signals. A DT of an asynchronous motor is built based on finite element method, and experiments are conducted under multiple few-shot settings and three working conditions. Comparative and ablation studies demonstrate the superiority and effectiveness of the proposed method for few-shot fault diagnosis.

Problem

• few-shot fault diagnosis
• digital twin
• intelligent fault diagnosis
• limited labeled data
• industrial machinery

Innovation

• bi-directional digital twin
• few-shot fault diagnosis
• prototype anchoring
• multi-periodicity learning
• test-time adaptation