Unsupervised Feature Engineering in Imbalanced and Unlabeled Mooring Monitoring Data for Augmentation and Translation by Hierarchical Variational Approach

Abstract

The stability of mooring systems in floating offshore wind turbines (FOWTs) is essential, as any deterioration affects platform performance and overall efficiency. Machinelearning monitoring of FOWTs requires comprehensive dynamic response data derived from various mooring systems. Data collection should encompass a variety of health states, operational scenarios, and metocean conditions. Data is scarce in practice, especially concerning damage-associated information. Additionally, the existing imbalanced dataset lacks clear labels, making it difficult to differentiate between healthy (majority) and damage-associated (minority) instances. We propose a novel deep generative model (DGM) to efficiently deal with the challenge of unsupervised labeling of imbalanced data in the initial stage. It will subsequently be used to augment the labeled data from the previous stage and generate damage-associated data for a new (target) mooring system based solely on the healthy records of that system. The conditional hierarchical variational autoencoder (CHVAE) is developed using a diffusion probabilistic architecture during a pretrain-finetune training procedure. During the pre-training stage, trained on imbalanced, unlabeled data, we employed an unsupervised feature engineering approach in the latent space created to identify the healthy (majority) and damaged (minority) data. In the fine-tuning stage, the nonlinear relationship between healthy and minority-damaged responses of a mooring system is obtained, considered as the source domain across diverse sea states. Subsequently, by employing the healthy data from the target mooring system, the CHVAE can generate real-scale damaged responses of the system across diverse operational and environmental conditions. An analysis is conducted to evaluate the similarity between the simulated records produced by OpenFast on the OC4-DeepCWind FOWT benchmark and those generated by the CHVAE framework, utilizing visual, statistical, and behavioral methods. Furthermore, the proposed DGM’s performance is first assessed using MNIST benchmark image dataset to evaluate its effectiveness for unsupervised labeling and data augmentation. The generated records for unobserved random sea states closely resemble real-world dynamic behaviors during downstream binary classification, illustrating the effectiveness and versatility of the proposed DGM, CHVAE.