Fault Detection By Segment Evaluation Based On Inferential Statistics For Asset Monitoring

Vepa Atamuradov, Kamal Medjaher, Benjamin Lamoureux, Pierre Dersin, and Noureddine Zerhouni
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Full Paper
phmc_17_007.pdf1.38 MBSeptember 6, 2017 - 12:32pm

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 information fusion algorithm, extracted features are combined to get a generic and representative feature. Afterward, combined feature is divided into homogeneous segments by sliding window segmentation algorithm. Finally, each segment is further evaluated by inferential statistics test to evaluate health state changes that indicate asset faults. To illustrate its effectiveness, the methodology is implemented on point machines 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 from degradation data for asset monitoring.

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Submission Keywords: 
fault detection
feature extraction
Point machine monitoring
Time series segmentation
Segment evaluation
Li-ion Battery health assessment
feature fusion
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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