用扩散模型生成多变量故障数据，解决新系统无故障样本难题。
Multivariate Data Augmentation for Predictive Maintenance using Diffusion
- 基于扩散模型学习健康与故障数据关系，生成合成故障数据。
- 在无历史故障数据的新系统上成功生成可用的多变量合成数据。
- 适用于工业、医疗等需早期故障预测的场景。
预测性维护广泛应用于工业、医疗和金融领域，依赖于对关键系统异常的持续检测与预测能力。当前AI模型虽能有效识别系统故障，但因组织力求最小化故障发生与停机时间，实际故障数据稀缺。对于新部署系统，更缺乏故障数据。本文提出利用扩散模型生成高维合成故障数据，以补充训练数据，提升异常检测性能。通过学习相似系统中健康与故障数据的关联，该模型可将此规律迁移至无故障数据的新系统，生成具有实用价值的合成故障数据，使预测模型得以训练。实验证明，该方法能有效生成适用于尚未发生故障系统的多变量合成数据。
原文摘要 · Abstract (English)
Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been trained to detect system faults, improving predictive maintenance efficiency. Typically there is a lack of fault data to train these models, due to organizations working to keep fault occurrences and down time to a minimum. For newly installed systems, no fault data exists since they have yet to fail. By using diffusion models for synthetic data generation, the complex training datasets for these predictive models can be supplemented with high level synthetic fault data to improve their performance in anomaly detection. By learning the relationship between healthy and faulty data in similar systems, a diffusion model can attempt to apply that relationship to healthy data of a newly installed system that has no fault data. The diffusion model would then be able to generate useful fault data for the new system, and enable predictive models to be trained for predictive maintenance. The following paper demonstrates a system for generating useful, multivariate synthetic data for predictive maintenance, and how it can be applied to systems that have yet to fail.
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