TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Dorka, Frithjof
A1  - Lucke, Dominik
A1  - Richards, Grant
T1  - A hybrid, distributed condition monitoring system using MEMS microphones, artificial neural networks, and cloud computing
JF  - Procedia CIRP
N2  - 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.
KW  - condition monitoring system
KW  - artificial neural networks
KW  - cloud computing
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-45020
SN  - 2212-8271
SS  - 2212-8271
U6  - https://doi.org/10.1016/j.procir.2023.06.024
DO  - https://doi.org/10.1016/j.procir.2023.06.024
VL  - 118
IS  - 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME ‘22, Italy
SP  - 134
EP  - 138
S1  - 5
PB  - Elsevier
CY  - Amsterdam
ER  -