Multivariate Data Augmentation for Predictive Maintenance using Diffusion
This addresses a data scarcity issue in predictive maintenance for industrial, medical, and financial domains, but it is incremental as it applies an existing method (diffusion models) to a new application area.
The paper tackles the problem of insufficient fault data for training AI models in predictive maintenance by using diffusion models to generate synthetic fault data, enabling anomaly detection for newly installed systems that have yet to fail.
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.