Next Article in Journal
Changing Sci from Post-Publication Peer-Review to Single-Blind Peer-Review
Previous Article in Journal
Lasers for Satellite Uplinks and Downlinks
Article

A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Department of Electrical Engineering and Computer Science, Knowledge-Based Systems and Knowledge Management, University of Siegen, 57076 Siegen, Germany
*
Author to whom correspondence should be addressed.
Received: 23 July 2020 / Accepted: 29 July 2020 / Published: 9 October 2020
(This article belongs to the Special Issue Data Science for Industry 4.0. Theory and Applications)
In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings. View Full-Text
Keywords: industry4.0; fault detection; fault diagnosis; random forest; diagnostic graph; distributed diagnosis; model-based; data-driven; hybrid approach; hydraulic test rig industry4.0; fault detection; fault diagnosis; random forest; diagnostic graph; distributed diagnosis; model-based; data-driven; hybrid approach; hydraulic test rig
Show Figures

Figure 1

MDPI and ACS Style

Mallak, A.; Fathi, M. A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest. Sci 2020, 2, 75. https://doi.org/10.3390/sci2040075

AMA Style

Mallak A, Fathi M. A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest. Sci. 2020; 2(4):75. https://doi.org/10.3390/sci2040075

Chicago/Turabian Style

Mallak, Ahlam, and Madjid Fathi. 2020. "A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest" Sci 2, no. 4: 75. https://doi.org/10.3390/sci2040075

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop