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Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning
Deep Learning has already been successfully applied to analyze industrial sensor data in a variety of relevant use cases.
Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts
Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks.
Towards Scenario-based Safety Validation for Autonomous Trains with Deep Generative Models
A common approach is to conduct safety validation based on a predefined Operational Design Domain (ODD) describing specific conditions under which a system under test is required to operate properly.
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research
In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
