TY  - JOUR
A1  - Kammerer, Klaus
A1  - Hoppenstedt, Burkhard
A1  - Pryss, Rüdiger
A1  - Stökler, Steffen
A1  - Allgaier, Johannes
A1  - Reichert, Manfred
T1  - Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings
JF  - Sensors
N2  - o build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.
KW  - anomaly detection
KW  - sensor data
KW  - machine learning
KW  - production machines
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193885
SN  - 1424-8220
VL  - 19
IS  - 24
ER  - 
TY  - JOUR
A1  - Grohmann, Johannes
A1  - Herbst, Nikolas
A1  - Chalbani, Avi
A1  - Arian, Yair
A1  - Peretz, Noam
A1  - Kounev, Samuel
T1  - A Taxonomy of Techniques for SLO Failure Prediction in Software Systems
JF  - Computers
N2  - Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.
KW  - taxonomy
KW  - survey
KW  - failure prediction
KW  - anomaly prediction
KW  - anomaly detection
KW  - self-aware computing
KW  - self-adaptive systems
KW  - performance prediction
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200594
SN  - 2073-431X
VL  - 9
IS  - 1
ER  -