Growing Structure Multiple Model System Based Anomaly Detection for Crankshaft Monitoring
While conventional approaches to diagnostics focus on detecting and identifying situations or behaviors which have previously been known to occur or can be anticipated, anomaly detection focuses on detecting and quantifying deviations away from learned “normal” behavior. A new anomaly detection scheme based on Growing Structure Multiple Model System(GSMMS) is utilized in this paper to detect and quantify the effects of slowly evolving anomalies on the crankshaft dynamics in a internal combustion engine. The Voronoi sets defined by the reference vectors of the growing Self-Organizing Networks(SONs), on which the GSMMS is based, naturally form a partition of the system operation space. Regionalization of system operation space using SONs makes it possible to model the system dynamics locally using simple models. In addition, the residual errors can be analyzed locally to accommodate unequally distributed residual errors in different regions.
KeywordsResidual Error Engine Speed Anomaly Detection Exponentially Weight Move Average Load Torque
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