STEP embeds progressive time series into a manifold between orthogonal prototypes so that polar angle tracks irreversible state progression and radius tracks mode via self-supervised contrastive learning.
Prog- nostics and health management design for rotary machinery systems—reviews, methodology and applications.Mechanical Systems and Signal Processing, 42(1-2):314–334, 2014
2 Pith papers cite this work. Polarity classification is still indexing.
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Picid is a new modular evaluation infrastructure that enforces deterministic, leakage-safe dataset construction and unified protocols for fault detection, diagnostics, and prognostics across twelve datasets and thirteen models.
citing papers explorer
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STEP: Learning STructured Embeddings for Progressive Time Series
STEP embeds progressive time series into a manifold between orthogonal prototypes so that polar angle tracks irreversible state progression and radius tracks mode via self-supervised contrastive learning.
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Picid: A Modular Evaluation Infrastructure for Reproducible PHM Across Tasks and Domains
Picid is a new modular evaluation infrastructure that enforces deterministic, leakage-safe dataset construction and unified protocols for fault detection, diagnostics, and prognostics across twelve datasets and thirteen models.