Detection of unexpected events (e.g. anomalies and faults) from monitoring data is very challenging in machine health assessment. Hence, abrupt or incipient fault detection from the monitoring data is very crucial to increase asset safety, availability and reliability. This paper presents a generic methodology for abrupt and incipient fault detection and feature fusion for health assessment of complex systems. Proposed methodology consists of feature extraction, feature fusion, segmentation and fault detection steps. First of all, different features are extracted using descriptive statistics. Secondly, based on linearly weighted data fusion algorithm, extracted features are combined to get the generic and representative feature. Afterward, combined feature is divided into homogeneous segments by sliding window segmentation algorithm. Finally, each segment is further evaluated by coefficient of variability which is used in inferential statistics, to evaluate health state changes that indicate asset faults. To illustrate its effectiveness, the methodology is implemented on point machine and Li-ion battery monitoring data to detect abrupt and incipient faults. The results show that proposed methodology can be effectively used in fault detection for asset monitoring.
How to Cite
fault detection, feature extraction, Point machine monitoring, Time series segmentation, Segment evaluation, Li-ion Battery health assessment, feature fusion
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