Abstract: To address the challenges of frequent operating condition changes, limited fault acoustic samples, and data imbalance in the anomaly detection of pumped storage units, this paper proposes an improved Vision Transformer (ViT)-based method for abnormal acoustic signal detection. First, the Mel-spectrogram algorithm was employed to convert one-dimensional acoustic signals into two-dimensional spectrograms, enriching the information content of the fault samples. These spectrograms were then fed into the ViT network, which leveraged the interaction mechanism between the self-attention layers and image features to learn features that were invariant across multiple operating conditions. Furthermore, a domain prompt and prompt adaptation module was introduced. This module predicts the unit′s status in the target domain by assessing feature similarities between the source and target domains. Experimental results on a real-world dataset demonstrate that the proposed method achieves an average accuracy of 90.0%, a recall of 87.9%, and an F₁-score of 0.887. On the MIMII public dataset, it outperforms other comparative methods, improving accuracy by 8.7%, recall by 6.92%, and F₁-score by 4.52% on average. Therefore, the proposed model effectively accomplishes anomaly detection tasks under conditions of multiple operating states and limited fault samples.