UBC Research Data

The Arc Loss Dataset Ibrahim Yousef; Lee D. Rippon; Carole Prévost; Sirish L. Shah; R. Bhushan Gopaluni

Description

The Arc Loss Dataset is a benchmark dataset designed for time series classification in industrial applications, particularly for fault detection and diagnosis (FDD). It was collected from a large-scale pyrometallurgical plant and captures the real-world complexities of industrial processes, including high dimensionality, sensor noise, and variable system dynamics. The dataset consists of 3,226 multivariate time series samples, each containing 1,101 time steps (equivalent to 55 minutes) with 96 process variables. The dataset is split into three subsets: I) train.pt (70% of the samples, 2,258 samples), II) val.pt (10%, 323 samples), and III) test.pt (20%, 645 samples). More information can be found in the README.txt

Item Metadata

Title	The Arc Loss Dataset
Creator	Ibrahim Yousef; Lee D. Rippon; Carole Prévost; Sirish L. Shah; R. Bhushan Gopaluni
Contributor	Ibrahim
Date Issued	2025-02-03
Description	The Arc Loss Dataset is a benchmark dataset designed for time series classification in industrial applications, particularly for fault detection and diagnosis (FDD). It was collected from a large-scale pyrometallurgical plant and captures the real-world complexities of industrial processes, including high dimensionality, sensor noise, and variable system dynamics. The dataset consists of 3,226 multivariate time series samples, each containing 1,101 time steps (equivalent to 55 minutes) with 96 process variables. The dataset is split into three subsets: I) train.pt (70% of the samples, 2,258 samples), II) val.pt (10%, 323 samples), and III) test.pt (20%, 645 samples). More information can be found in the README.txt
Subject	Computer and Information Science; Engineering; Time Series Classification; Fault Detection and Diagnosis; Industrial Benchmark; Process Monitoring
Type	Dataset
Date Available	2025-02-02
Provider	University of British Columbia Library
License	CC BY-NC 4.0
DOI	10.14288/1.0447904
URI	https://doi.org/10.5683/SP3/NREPZM
Publisher DOI	https://doi.org/10.5683/SP3/NREPZM
Rights URI	http://creativecommons.org/licenses/by-nc/4.0
Aggregated Source Repository	Dataverse

Item Media

Item Citations and Data

Licence

CC BY-NC 4.0

Open Collections