RESEARCH PAPER
TMDANN-A: Boundary-aware discriminative alignment and condition-guided multi-source at-tention for vibration-based bearing fault diagnosis under variable operating conditions
1
xinjiang university, China
2
Chengdu Institute of Special Equipment Inspection and Testing, China
3
Shanghai University of Electric Power, China
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2026-03-21
Final revision date: 2026-05-20
Acceptance date: 2026-07-11
Online publication date: 2026-07-29
KEYWORDS
Condition monitoringRolling bearingFault diagnosisVariable operating conditionsMulti-source domain adapta-tion
TOPICS
ABSTRACT
Rolling bearing fault diagnosis is important for condition monitoring of rotating machinery. In practice, variations in operating conditions and heterogeneity among source domains of-ten cause distribution shifts in vibration signals, while labeled target-domain data are usually unavailable. To address this issue, this paper proposes TMDANN-A, a multi-source domain adaptation framework for vibration-based bearing fault diagnosis. The framework combines adversarial domain alignment, boundary-aware discriminative learning, and condition-guided source weighting to improve domain invariance and class separability. A hierarchical CNN-Transformer encoder is used to capture local impulsive features and long-range tem-poral dependencies. Experiments on the JNU and PU datasets achieve 98.51% average accu-racy over six transfer tasks. On an industrial test-rig dataset, the method further achieves 95.42% average accuracy, 95.44% Macro-F1, and 95.22% mean fault recall. The results verify its effectiveness under complex operating conditions.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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