6 citations · 8 across the 5 of their papers we have counts for
6 papers
Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data
Abigail Langbridge, Fearghal O'Donncha, James T Rayfield +1
Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based framework for anomaly detectio…
PRIM: Meta-Learned Bayesian Root Cause Analysis
Christopher Lohse, Anish Dhir, Amadou Ba +3
Root cause analysis (RCA) in complex systems is challenging due to error propagation across multiple variables, the need for structural causal knowledge, and the computational cost…
From Documents to Database: Failure Modes for Industrial Assets
Duygu Kabakci-Zorlu, Fabio Lorenzi, John Sheehan +2
We propose an interactive system using foundation models and user-provided technical documents to generate Failure Mode and Effects Analyses (FMEA) for industrial equipment. Our sy…
FMEA Builder: Expert Guided Text Generation for Equipment Maintenance
Karol Lynch, Fabio Lorenzi, John Sheehan +2
Foundation models show great promise for generative tasks in many domains. Here we discuss the use of foundation models to generate structured documents related to critical assets.…
Two-sample KS test with approxQuantile in Apache Spark
Bradley Eck, Duygu Kabakci-Zorlu, Amadou Ba
The classical two-sample test of Kolmogorov-Smirnov (KS) is widely used to test whether empirical samples come from the same distribution. Even though most statistical packages pro…
What Makes a Good Dataset for Symbol Description Reading?
Karol Lynch, Joern Ploennigs, Bradley Eck
The usage of mathematical formulas as concise representations of a document's key ideas is common practice. Correctly interpreting these formulas, by identifying mathematical symbo…