In:
Chemie Ingenieur Technik, Wiley, Vol. 95, No. 7 ( 2023-07), p. 1077-1082
Abstract:
This paper provides the first comprehensive evaluation and analysis of modern (deep‐learning‐based) unsupervised anomaly detection methods for chemical process data. We focus on the Tennessee Eastman process dataset, a standard litmus test to benchmark anomaly detection methods for nearly three decades. Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications. From the benchmark, we conclude that reconstruction‐based methods are the methods of choice, followed by generative and forecasting‐based methods.
Type of Medium:
Online Resource
ISSN:
0009-286X
,
1522-2640
DOI:
10.1002/cite.202200238
Language:
English
Publisher:
Wiley
Publication Date:
2023
detail.hit.zdb_id:
215592-8
detail.hit.zdb_id:
2035041-7
