Two-Stage Self-Supervised Meta-Learning for Few-Shot Cross-Condition Fault Diagnosis of Air Conditioning Compressors

The air conditioning compressor serves as the primary power unit in refrigeration systems. Vibration data acquired from vibration sensors can be used to implement fault diagnosis, and reliable fault diagnosis is critical for ensuring safe and stable system operation. However, existing data-driven approaches face two fundamental bottlenecks: the prohibitive cost of acquiring large-scale labeled fault samples in industrial settings, and the degradation of model generalization caused by a domain shift in vibration signals across different operating conditions (e.g., high-pressure vs. low-pressure modes). To address these challenges, this paper proposes a Two-Stage Self-Supervised Meta-Learning (TS-SSML) framework. In Stage 1, we perform self-supervised contrastive pre-training on large-scale unlabeled vibration signals using the Normalized Temperature-Scaled Cross-Entropy (NT-Xent) loss, enabling the encoder to learn fault representations that are transferable across operating conditions. In Stage 2, the pre-trained encoder is frozen, and an Attention-Weighted Prototypical Network (AWPN) is trained via episodic meta-learning to dynamically assess the contribution of each support sample to class prototypes, thereby enhancing discriminability in few-shot classification. We conduct systematic evaluations on a self-collected AC compressor vibration dataset across two tasks: cross-condition transfer (Task T1, 10-way) and incremental few-shot novel-class detection (Task T2, 6-known-to-4-novel). Experimental results demonstrate that the proposed method achieves a 96.50% (T1) and 94.10% (T2) classification accuracy under the 5-shot setting, outperforming standard Prototypical Networks by 1.20% and 4.92%, respectively. Compared to Sparse Coding-based Fault Diagnosis (SCM-FD), our method reduces the episodic meta-training time by 93.75% (Stage 2 few-shot adaptation only) while achieving superior accuracy. These results highlight the substantial advantages of the proposed framework in industrial fault diagnosis scenarios with scarce labeled data.