FreqCondNorm: Towards Cross-domain Predictive Maintenance through a Frequency-Conditioned Transformer Foundation Model

2026-09-17

AI Summary

为解决跨域预测性维护中模型迁移性差的问题，提出基于Transformer的FreqCondNorm模型，通过频率条件归一化层统一处理不同采样频率的时间序列数据。

Abstract

Deep learning predictive maintenance models suffer from poor transferability across machines and operating conditions, especially when labelled data are scarce and signals span five orders of magnitude in sampling frequency (1 Hz to ~100 kHz). We propose FreqCondNorm, a Transformer-based architecture that introduces a FiLM-style frequency-conditioned normalization layer to unify heterogeneous time-series within a single model. The architecture is pretrained on five public predictive maintenance datasets (CWRU, MFPT, UOC18, PRONOSTIA, CMAPSS) using masked auto-encoding and contrastive learning with balanced domain sampling. On fault diagnosis, the model achieves 99.2% accuracy on CWRU (+6.4 pp over CNN) and 82.1% zero-shot accuracy on MFPT, demonstrating strong transfer across sampling frequencies. However, the approach does not improve remaining useful life prediction, suggesting a mismatch between pretraining and RUL objectives that warrants future investigation.

Problem

• predictive maintenance
• transferability
• sampling frequency

Innovation

• FreqCondNorm
• frequency-conditioned normalization
• transformer architecture
• cross-domain predictive maintenance
• masked auto-encoding