@misc{12812,
  abstract     = {{Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data.}},
  author       = {{Berns, Fabian and Lange-Hegermann, Markus and Beecks, Christian}},
  booktitle    = {{ Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1}},
  editor       = {{Panetto, H. and Madani, K. and Smirnov, A.}},
  isbn         = {{978-989-758-476-3}},
  keywords     = {{Anomaly Detection, Gaussian Processes, Explainable Machine Learning, Industry 4.0}},
  location     = {{Budapest, HUNGARY}},
  pages        = {{87--92}},
  publisher    = {{SCITEPRESS - Science and Technology Publications}},
  title        = {{{Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0}}},
  doi          = {{10.5220/0010130300870092}},
  year         = {{2020}},
}

@misc{12808,
  abstract     = {{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.}},
  author       = {{Li, Peng and Niggemann, Oliver}},
  booktitle    = {{Engineering Applications of Artificial Intelligence}},
  issn         = {{1873-6769}},
  keywords     = {{One-class classification, n-dimensional oriented non-convex hull, Anomaly detection, CPPS}},
  publisher    = {{Elsevier BV}},
  title        = {{{Non-convex hull based anomaly detection in CPPS}}},
  doi          = {{10.1016/j.engappai.2019.103301}},
  volume       = {{87}},
  year         = {{2019}},
}

@inproceedings{2167,
  abstract     = {{Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly. The analysis and diagnosis of such systems is a major part of this trend: Plants should detect automatically wear, faults and suboptimal configurations. This paper reflects the current state-of- the-art in diagnosis against the requirements of CPPSs, identifies three main gaps and gives application scenarios to outline first ideas for potential solutions to close these gaps.
}},
  author       = {{Niggemann, Oliver and Lohweg, Volker}},
  booktitle    = {{Twenty-Ninth Conference on Artificial Intelligence (AAAI-15)}},
  keywords     = {{Cyber-Physical Systems, Machine Learning, Diagnosis, Anomaly Detection}},
  title        = {{{On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda}}},
  year         = {{2015}},
}