@article{DorkaLuckeRichards2023,
  author    = {Dorka, Frithjof and Lucke, Dominik and Richards, Grant},
  title     = {A hybrid, distributed condition monitoring system using MEMS microphones, artificial neural networks, and cloud computing},
  journal   = {Procedia CIRP},
  volume    = {118},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2023.06.024},
  institution = {NXT Nachhaltigkeit und Technologie},
  pages     = {134 -- 138},
  year      = {2023},
  abstract  = {Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance. However, the cost of traditional sensors, data acquisition systems, and the required information technology expert-knowledge challenge the industry. This paper presents a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The system's feasibility is validated using a testbed for reciprocating linear-motion axes.},
  language  = {en}
}