Multivariate Data Augmentation for Predictive Maintenance using Diffusion

TL;DR

This paper introduces a diffusion-based multivariate data augmentation method to generate synthetic fault data, enhancing predictive maintenance models especially for new systems lacking fault data.

Contribution

It presents a novel diffusion model approach for generating synthetic fault data to improve predictive maintenance in systems with limited or no fault data.

Findings

Synthetic fault data improves anomaly detection accuracy.

The method enables predictive modeling for new systems without prior fault data.

Diffusion models effectively learn relationships between healthy and faulty data.

Abstract

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.