This work explores the application of pre-trained time-series foundation models (FMs) for detecting anomalies in industrial processes and introduces a new time-series forecasting method that filters out suspicious data and uses previously predicted data as input, called Forecast Fallback (FF).
This study develops and evaluates an AI-based analytical system for detecting anomalies in industrial processes. The work reviews major sources of risk in industrial control systems, distinguishes point, contextual, and collective anomalies, and summarizes the principal machine-learning approaches used for industrial a...
Mehdiyeva Almaz, Ahmedov Elmar, Uzakov Gulom et al.· 2026 International Conferenc...· 0 citations
This work presents a protocol-aware empirical assessment across three settings: a C-MAPSS degradation-risk proxy, normal-only training for anomalous-sound detection on MIMII, and BDG2 forecasting-residual diagnostics with synthetic target perturbations.
A structured methodology for ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models is proposed, offering valuable insights for developing efficient and scalable PdM solutions.
Sithik Shah· International Journal of App...· 0 citations
This paper presents a method that addresses the imbalance between normal and anomaly data by transforming time-series data into a structured sentence format and using a large language model to generate and augment diverse anomaly scenarios.
Geunho Lee, Jieun Lee, Tae-yong Kim et al.· Applied Sciences· 0 citations
This work investigates the zero-shot application of a univariate forecasting FM, TimesFM, to industrial MTSAD on the Secure Water Treatment (SWaT) benchmark, and concludes that the proposed naive zero-shot FMs are unsuitable for MTSAD but promising for change-point detection.
M. Uray, S. Messineo, Roland Kwitt et al.· arXiv.org· 0 citations
The findings highlight the importance of training-data integrity in ML-enabled ICS monitoring, subject to the evaluated dataset, models, and threat assumptions, and show that robustness is strongly model-dependent and cannot be predicted from clean-data performance alone.
M. Ozbek, Taiwo P. Ojo, Pooria Madani et al.· 0 citations
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