Journal article
David A. Quispe G, Fahimeh Rajabiyazdi, G. Jamieson
IEEE International Conference on Systems, Man and Cybernetics, 2020
IEEE International Conference on Systems, Man and Cybernetics, 2020
Semantic Scholar
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APA
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G, D. A. Q., Rajabiyazdi, F., & Jamieson, G. (2020). A Machine Learning-Based Micro-World Platform for Condition-Based Maintenance. IEEE International Conference on Systems, Man and Cybernetics.
Chicago/Turabian
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G, David A. Quispe, Fahimeh Rajabiyazdi, and G. Jamieson. “A Machine Learning-Based Micro-World Platform for Condition-Based Maintenance.” IEEE International Conference on Systems, Man and Cybernetics (2020).
MLA
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G, David A. Quispe, et al. “A Machine Learning-Based Micro-World Platform for Condition-Based Maintenance.” IEEE International Conference on Systems, Man and Cybernetics, 2020.
BibTeX Click to copy
@article{david2020a,
title = {A Machine Learning-Based Micro-World Platform for Condition-Based Maintenance},
year = {2020},
journal = {IEEE International Conference on Systems, Man and Cybernetics},
author = {G, David A. Quispe and Rajabiyazdi, Fahimeh and Jamieson, G.}
}
Abstract
The use of machine learning algorithms is surging in industrial condition-based maintenance systems. However, machine learning algorithms are often considered a black box undermining the adoption of these systems by creating human-automation interaction challenges. To address this opacity issue, we must explain and describe the logic and outcome of the algorithms (known as explainability and transparency). Existing design practices are not effective in meeting these requirements; thus, more user-based experiments should be conducted to identify effective design practices. Experimental platforms that support the empirical validation of transparency and explainability approaches are extremely limited both in academia and industry. We propose an open-source platform to assist researchers in conducting human-subjects experiments.