TL;DR
Fault diagnosis contrastive language-signal pre-training (FD-CLSP) is proposed, a zero-shot cross-domain framework for bearing fault diagnosis under unseen operating conditions that leverages the noise-robustness and domain-invariance of high-level semantics to guide the extraction of elusive physical features.
A novel and robust Source-Free Domain Adaptation (SFDA) framework equipped with post-hoc explainability for bearing fault diagnosis is proposed, and a post-hoc visualization mechanism is incorporated to explain the model's decision-making process, enhancing the transparency and credibility of the diagnosis for practica...
Severe noise in industrial environments often leads to distribution shifts, posing a critical challenge for deep learning-based bearing fault diagnosis. Models trained on clean data typically degrade under such conditions due to their inability to effectively disentangle noise from fault-related features. To address th...
Zero-shot industrial anomaly detection (ZIAD) aims to develop a unified model capable of directly identifying unseen anomaly categories in images without requiring reference samples. Recently, large-scale Vision-Language Models (VLMs) such as CLIP have shown great potential for solving this task. However, existing meth...
Real-world data-driven fault diagnosis is constrained by scarce fault samples, limited and imbalanced (L&I) class distributions, and heterogeneous multisensor data under operating condition shifts, which impede stable and generalizable discriminative representation learning. We propose the local feature alignment multi...
Vibration-signal-based fault diagnosis in practical rotating machinery and aeroengine systems is often challenged by limited labeled fault samples, variable working conditions, unseen fault modes, and cross-equipment distribution shifts. These common task-level difficulties make few-shot, open-set, cross-domain diagnos...
Intelligent industrial systems require reliable condition monitoring when operating conditions change, yet labeled fault data from a new condition are often scarce. This paper presents LACoFD, a leakage-aware contrastive few-shot intelligent diagnosis framework for cross-condition rotating machinery. The framework lear...
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