Robust Contrastive Learning and Multi-shot Voting for High-dimensional Multivariate Data-driven Prognostics

Abstract

The availability of data gathered from industrial sensors has increased expeditiously in recent years. These data are valuable assets in delivering exceptional services for manufacturing enterprises. We see growing interests and expectations from manufacturers in deploying artificial intelligence for predictive maintenance. The paper has adopted and transferred a state-of-the-art method from few-shot learning to failure prognostics using the Siamese neural network based contractive learning. The method has three main characteristics on top of the highest performance - a sensitivity of 98.4% for Scania truck's air pressure system failure capture, compared to the methods proposed by the previous related research: prediction stability, deployment flexibility, and the robust multi-shot diagnosis based on selected historical reference samples.