  Unsupervised fault detection in multivariate time series is critical for
maintaining the integrity and efficiency of complex systems, with current
methodologies largely focusing on statistical and machine learning techniques.
However, these approaches often rest on the assumption that data distributions
conform to Gaussian models, overlooking the diversity of patterns that can
manifest in both normal and abnormal states, thereby diminishing discriminative
performance. Our innovation addresses this limitation by introducing a
combination of data augmentation and soft contrastive learning, specifically
designed to capture the multifaceted nature of state behaviors more accurately.
The data augmentation process enriches the dataset with varied representations
of normal states, while soft contrastive learning fine-tunes the model's
sensitivity to the subtle differences between normal and abnormal patterns,
enabling it to recognize a broader spectrum of anomalies. This dual strategy
significantly boosts the model's ability to distinguish between normal and
abnormal states, leading to a marked improvement in fault detection performance
across multiple datasets and settings, thereby setting a new benchmark for
unsupervised fault detection in complex systems. The code of our method is
available at \url{https://github.com/zangzelin/code_USD.git}.
