A Machine Learning-Based Micro-World Platform for Condition-Based Maintenance

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)(2020)

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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.
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Key words
micro-world,experimental platform,machine learning,transparency,explainable artificial intelligence,condition-based maintenance,decision support systems
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