PerFormer: A Permutation Based Vision Transformer for Remaining Useful Life Prediction

• URL: http://arxiv.org/abs/2506.00259v2
• Date: Fri, 13 Jun 2025 23:12:32 GMT
• Title: PerFormer: A Permutation Based Vision Transformer for Remaining Useful Life Prediction
• Authors: Zhengyang Fan, Wanru Li, Kuo-chu Chang, Ting Yuan,
• Abstract summary: We introduce the PerFormer, a permutation-based vision transformer approach designed to permute multivariate time series data.<n>Our experiments on NASA's C-MAPSS dataset demonstrate the PerFormer's superior performance in RUL prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately estimating the remaining useful life (RUL) for degradation systems is crucial in modern prognostic and health management (PHM). Convolutional Neural Networks (CNNs), initially developed for tasks like image and video recognition, have proven highly effectively in RUL prediction, demonstrating remarkable performance. However, with the emergence of the Vision Transformer (ViT), a Transformer model tailored for computer vision tasks such as image classification, and its demonstrated superiority over CNNs, there is a natural inclination to explore its potential in enhancing RUL prediction accuracy. Nonetheless, applying ViT directly to multivariate sensor data for RUL prediction poses challenges, primarily due to the ambiguous nature of spatial information in time series data. To address this issue, we introduce the PerFormer, a permutation-based vision transformer approach designed to permute multivariate time series data, mimicking spatial characteristics akin to image data, thereby making it suitable for ViT. To generate the desired permutation matrix, we introduce a novel permutation loss function aimed at guiding the convergence of any matrix towards a permutation matrix. Our experiments on NASA's C-MAPSS dataset demonstrate the PerFormer's superior performance in RUL prediction compared to state-of-the-art methods employing CNNs, Recurrent Neural Networks (RNNs), and various Transformer models. This underscores its effectiveness and potential in PHM applications.

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