Reliable anomaly detection under data contamination remains a major challenge in Prognostics and Health Management, especially when degradation processes are gradual and clean training data are unavailable. This paper introduces a weighted iterative refinement framework with autoencoders for contaminated anomaly detection (WIRACAD) to address this problem. The method, which is based on reconstruction residuals, re-weights training samples across iterations in order to progressively reduce the influence of suspected anomalies. This continuous refinement improves the robustness of health indicator learning from contaminated time series. The proposed approach is evaluated on two public benchmarks: the NASA C-MAPSS dataset and the IMS Bearing dataset. Results show consistent improvements in key metrics related to degradation monitoring. In particular, the overal fit score and the monotonicty are improved when compared to baseline autoencoder training and recent refinement-based methods. These findings suggest that iterative sample weighting can enhance unsupervised anomaly detection with autoencoders in settings where data contamination is assumed.

Time series semantic segmentation is the task of extracting time intervals from the time series data that share a similar meaning within the application domain in an unsupervised manner. State-of-the-art algorithms typically treat this problem as change point detection, resulting in discrete state transitions. However, in real-world applications, states often transition gradually. This leads to a novel, more challenging variation of the traditional time series segmentation task, for which we present PaTSS, a novel, domain-agnostic algorithm to uncover those gradual state transitions. PaTSS learns a distribution over the semantic segments based on an embedding space derived from mined sequential patterns. An extensive experimental evaluation on 107 benchmark time series shows that PaTSS is capable of detecting gradual state transitions, a task current methods are unable to perform.

Regularization is one of the key concepts in machine learning, but so far it has received only little attention in the logical and relational learning setting. Here we propose a regularization and feature selection technique for such setting, in which one commonly represents the structure of the domain using an entity-relationship model. To this end, we introduce a notion of locality that ties together features according to their proximity in a transformed representation of the relational learning problem obtained via a procedure that we call “graphicalization”. We present two techniques, a wrapper and an efficient embedded approach, to identify the most relevant sets of predicates which yields more readily interpretable results than selecting low-level propositionalized features. The proposed techniques are implemented in the kernel-based relational learner kLog, although the ideas presented here can also be adapted to other relational learning frameworks. We evaluate our approach on classification tasks in the natural language processing and bioinformatics domain.