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WHODID: Web-based interface for Human-assisted factory Operations in fault Detection, Identification and Diagnosis

Pierre Blanchart 1 Cedric Gouy-Pailler 1
1 LADIS - Laboratoire d'analyse des données et d'intelligence des systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
Abstract : We present WHODID: a turnkey intuitive web-based interface for fault detection, identification and diagnosis in production units. Fault detection and identification is an extremely useful feature and is becoming a necessity in modern production units. Moreover, the large deployment of sensors within the stations of a production line has enabled the close monitoring of products being manufactured. In this context, there is a high demand for computer intelligence able to detect and isolate faults inside production lines, and to additionally provide a diagnosis for maintenance on the identified faulty production device, with the purpose of preventing subsequent faults caused by the diagnosed faulty device behavior. We thus introduce a system which has fault detection, isolation, and identification features, for retrospective and on-the-fly monitoring and maintenance of complex dynamical production processes. It provides real-time answers to the questions: " is there a fault? " , " where did it happen? " , " for what reason? ". The method is based on a posteriori analysis of decision sequences in XGBoost tree models, using recurrent neural networks sequential models of tree paths. The particularity of the presented system is that it is robust to missing or faulty sensor measurements, it does not require any modeling of the underlying, possibly exogenous manufacturing process, and provides fault diagnosis along with confidence level in plain English formulations. The latter can be used as maintenance directions by a human operator in charge of production monitoring and control.
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Pierre Blanchart, Cedric Gouy-Pailler. WHODID: Web-based interface for Human-assisted factory Operations in fault Detection, Identification and Diagnosis. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, Sep 2017, Skopje, Macedonia. pp.437-441, ⟨10.1007/978-3-319-71273-4_47⟩. ⟨cea-01832974⟩

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