Publication
Developing reliable health indicators for industrial assets is essential for accurate condition monitoring, fault detection, and predicting the remaining useful lifetime. However, constructing such indicators is challenging, especially given the increasing complexity of industrial systems and the not well-understood degradation dynamics. Previously proposed autoencoder-based methods for unsupervised health indicator construction faced the difficulty of constraining the latent representation over the system’s lifetime to obtain trendable and prognosable health indicators. Koopman operator theory provides a natural solution to this challenge. In this work, we first demonstrate the successful application of the Deep Koopman Operator approach for learning the dynamics of industrial systems. This results in a latent representation that provides sufficient information for estimating the remaining useful life of the asset. Secondly, we propose a novel Koopman-Inspired Degradation Model for modelling the degradation of dynamical systems with control. The Koopman-based algorithms demonstrate superior or comparable performance with autoencoder-based approaches in predicting the remaining useful life of assets such as CNC milling machine cutters and Li-ion batteries.