Utilizing AutomationML data on system topology for fault diagnosis with Case-Based Reasoning

Failure recovery plays an important role in improving Overall Equipment E ectiveness. However, in an increasingly dynamic market, failure recovery mechanisms need to be able to adapt to system changes. Starting with fault diagnosis in automated Production Systems for assembly and logistics, this paper proposes a novel approach to combining Model-based Reasoning on topological system models with Case-based Reasoning. The topological models are accessed via AutomationML and leveraged for case adaption, which significantly reduces the engineering e ort of adding new fault types to the system, compared to signal-based methods. Furthermore, the approach does not rely on complete fault models existing in advance; thus, the case database can be continuously built up during operation.     «

Failure recovery plays an important role in improving Overall Equipment E ectiveness. However, in an increasingly dynamic market, failure recovery mechanisms need to be able to adapt to system changes. Starting with fault diagnosis in automated Production Systems for assembly and logistics, this paper proposes a novel approach to combining Model-based Reasoning on topological system models with Case-based Reasoning. The topological models are accessed via AutomationML and leveraged for cas...     »