ROOT CAUSE ANALYSIS FOR DETERMINISTIC MACHINE LEARNING MODEL

ROOT CAUSE ANALYSIS FOR DETERMINISTIC MACHINE LEARNING MODEL

Techniques for identifying a root cause of an operational result of a deterministic machine learning model are disclosed. A system applies a deterministic machine learning model to a set of data to generate an operational result, such as a prediction of a "fault" or "no-fault" in...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: ROHRKEMPER, James Charles, BACLAWSKI, Kenneth Paul, WANG, Guang Chao, GAWLICK, Dieter, CHYSTIAKOVA, Anna, LIU, Zhen Hua, GROSS, Kenny C, SONDEREGGER, Richard Paul
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Beschreibung
Zusammenfassung:Techniques for identifying a root cause of an operational result of a deterministic machine learning model are disclosed. A system applies a deterministic machine learning model to a set of data to generate an operational result, such as a prediction of a "fault" or "no-fault" in the system. The set of data includes signals from multiple different data sources, such as sensors. The system applies an abductive model, generated based on the deterministic machine learning model, to the operational result. The abductive model identifies a particular set of data sources that is associated with the root cause of the operational result. The system generates a human-understandable explanation for the operational result based on the identified root cause.