---
_id: '12808'
abstract:
- lang: eng
  text: Along with the constantly increasing complexity of industrial automation systems,
    machine learning methods have been widely applied to detecting abnormal states
    in such systems. Anomaly detection tasks can be treated as one-class classification
    problems in machine learning. Geometric methods can give an intuitive solution
    to such problems. In this paper, we propose a new geometric structure, oriented
    non-convex hulls, to represent decision boundaries used for one-class classification.
    Based on this geometric structure, a novel boundary based one-class classification
    algorithm is developed to solve the anomaly detection problem. Compared with traditional
    boundary-based approaches such as convex hulls based methods and one-class support
    vector machines, the proposed approach can better reflect the true geometry of
    target data and needs little effort for parameter tuning. The effectiveness of
    this approach is evaluated with artificial and real world data sets to solve the
    anomaly detection problem in Cyber-Physical-Production-Systems (CPPS). The evaluation
    results also show that the proposed approach has higher generality than the used
    baseline algorithms.
article_number: '103301'
author:
- first_name: Peng
  full_name: Li, Peng
  id: '58937'
  last_name: Li
- first_name: Oliver
  full_name: Niggemann, Oliver
  id: '10876'
  last_name: Niggemann
citation:
  ama: Li P, Niggemann O. Non-convex hull based anomaly detection in CPPS. <i>Engineering
    Applications of Artificial Intelligence</i>. 2019;87. doi:<a href="https://doi.org/10.1016/j.engappai.2019.103301">10.1016/j.engappai.2019.103301</a>
  apa: Li, P., &#38; Niggemann, O. (2019). Non-convex hull based anomaly detection
    in CPPS. <i>Engineering Applications of Artificial Intelligence</i>, <i>87</i>,
    Article 103301. <a href="https://doi.org/10.1016/j.engappai.2019.103301">https://doi.org/10.1016/j.engappai.2019.103301</a>
  bjps: <b>Li P and Niggemann O</b> (2019) Non-Convex Hull Based Anomaly Detection
    in CPPS. <i>Engineering Applications of Artificial Intelligence</i> <b>87</b>.
  chicago: Li, Peng, and Oliver Niggemann. “Non-Convex Hull Based Anomaly Detection
    in CPPS.” <i>Engineering Applications of Artificial Intelligence</i> 87 (2019).
    <a href="https://doi.org/10.1016/j.engappai.2019.103301">https://doi.org/10.1016/j.engappai.2019.103301</a>.
  chicago-de: Li, Peng und Oliver Niggemann. 2019. Non-convex hull based anomaly detection
    in CPPS. <i>Engineering Applications of Artificial Intelligence</i> 87. doi:<a
    href="https://doi.org/10.1016/j.engappai.2019.103301">10.1016/j.engappai.2019.103301</a>,
    .
  din1505-2-1: '<span style="font-variant:small-caps;">Li, Peng</span> ; <span style="font-variant:small-caps;">Niggemann,
    Oliver</span>: Non-convex hull based anomaly detection in CPPS. In: <i>Engineering
    Applications of Artificial Intelligence</i> Bd. 87. Amsterdam [u.a.], Elsevier
    BV (2019)'
  havard: P. Li, O. Niggemann, Non-convex hull based anomaly detection in CPPS, Engineering
    Applications of Artificial Intelligence. 87 (2019).
  ieee: 'P. Li and O. Niggemann, “Non-convex hull based anomaly detection in CPPS,”
    <i>Engineering Applications of Artificial Intelligence</i>, vol. 87, Art. no.
    103301, 2019, doi: <a href="https://doi.org/10.1016/j.engappai.2019.103301">10.1016/j.engappai.2019.103301</a>.'
  mla: Li, Peng, and Oliver Niggemann. “Non-Convex Hull Based Anomaly Detection in
    CPPS.” <i>Engineering Applications of Artificial Intelligence</i>, vol. 87, 103301,
    2019, <a href="https://doi.org/10.1016/j.engappai.2019.103301">https://doi.org/10.1016/j.engappai.2019.103301</a>.
  short: P. Li, O. Niggemann, Engineering Applications of Artificial Intelligence
    87 (2019).
  ufg: '<b>Li, Peng/Niggemann, Oliver</b>: Non-convex hull based anomaly detection
    in CPPS, in: <i>Engineering Applications of Artificial Intelligence</i> 87 (2019).'
  van: Li P, Niggemann O. Non-convex hull based anomaly detection in CPPS. Engineering
    Applications of Artificial Intelligence. 2019;87.
date_created: 2025-04-16T09:51:12Z
date_updated: 2025-06-26T13:34:10Z
department:
- _id: DEP5023
doi: 10.1016/j.engappai.2019.103301
external_id:
  isi:
  - '000506715100040'
intvolume: '        87'
isi: '1'
keyword:
- One-class classification
- n-dimensional oriented non-convex hull
- Anomaly detection
- CPPS
language:
- iso: eng
place: Amsterdam [u.a.]
publication: Engineering Applications of Artificial Intelligence
publication_identifier:
  eissn:
  - 1873-6769
  issn:
  - 0952-1976
publication_status: published
publisher: Elsevier BV
status: public
title: Non-convex hull based anomaly detection in CPPS
type: scientific_journal_article
user_id: '83781'
volume: 87
year: '2019'
...