@article{KammererHoppenstedtPryssetal.2019,
  author    = {Kammerer, Klaus and Hoppenstedt, Burkhard and Pryss, R{\"u}diger and St{\"o}kler, Steffen and Allgaier, Johannes and Reichert, Manfred},
  title     = {Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings},
  series = {Sensors},
  volume    = {19},
  journal   = {Sensors},
  number    = {24},
  issn      = {1424-8220},
  doi       = {10.3390/s19245370},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193885},
  pages     = {5370},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{KammererPryssHoppenstedtetal.2020,
  author    = {Kammerer, Klaus and Pryss, R{\"u}diger and Hoppenstedt, Burkhard and Sommer, Kevin and Reichert, Manfred},
  title     = {Process-driven and flow-based processing of industrial sensor data},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {18},
  issn      = {1424-8220},
  doi       = {10.3390/s20185245},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213089},
  year      = {2020},
  abstract  = {For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.},
  language  = {en}
}
@article{PryssSchleeHoppenstedtetal.2020,
  author    = {Pryss, R{\"u}diger and Schlee, Winfried and Hoppenstedt, Burkhard and Reichert, Manfred and Spiliopoulou, Myra and Langguth, Berthold and Breitmayer, Marius and Probst, Thomas},
  title     = {Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study},
  series = {Journal of Medical Internet Research},
  volume    = {22},
  journal   = {Journal of Medical Internet Research},
  number    = {6},
  doi       = {10.2196/15547},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229517},
  year      = {2020},
  abstract  = {Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1\% to 42.7\% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient's quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)-Android and iOS-to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider.},
  language  = {en}
}