Articles | Volume 9, issue 1
https://doi.org/10.5194/jsss-9-143-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.Special issue:
Data-driven vibration-based bearing fault diagnosis using non-steady-state training data
Related authors
Related subject area
Applications: Automation
Human activity recognition system using wearable accelerometers for classification of leg movements: a first, detailed approach
Cutout as augmentation in contrastive learning for detecting burn marks in plastic granules
En route to automated maintenance of industrial printing systems: digital quantification of print-quality factors based on induced printing failure
An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques
Test method for narrowband F/TDMA-based wireless sensor/actuator networks including radio channel emulation in severe multipath environments
J. Sens. Sens. Syst., 13, 187–209,
2024J. Sens. Sens. Syst., 13, 63–69,
2024J. Sens. Sens. Syst., 11, 277–285,
2022J. Sens. Sens. Syst., 10, 153–162,
2021J. Sens. Sens. Syst., 7, 183–192,
2018Cited articles
Albarbar, A., Mekid, S., Starr, A., and Pietruszkiewicz, R.: Suitability of
MEMS accelerometers for condition monitoring: An experimental study,
Sensors, 8, 784–799, 2008. a
Antoni, J. and Randall, R.: The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mech. Syst. Signal Pr., 20, 308–331, 2006. a
Assaad, B., Eltabach, M., and Antoni, J.: Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes, Mech. Syst. Signal Pr., 42, 351–367, 2014. a
Bajric, R., Zuber, N., Skrimpas, G., and Mijatovic, N.: Feature Extraction
Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine
Gearbox, Shock and Vibration, 2016, 6748469, https://doi.org/10.1155/2016/6748469, 2016. a, b