Integrated multivariate health monitoring system for helicopters main rotor drives: development and validation with in-service data

Alberto Bellazzi, Giovanni Jacazio, Bruno Maino, Gueorgui Myhailov, Franco Pellerey, and Massimo Sorli
Submission Type: 
Full Paper
phmc_14_045.pdf353.25 KBSeptember 17, 2014 - 12:27pm

The implementation into service of accelerometric health monitoring systems of mechanical power drives on helicopters has shown that the generation of false failure alarms is a critical issue. The paper presents a combined application of several multivariate statistical techniques and shows how a monitoring method which integrates these tools can be successfully exploited in order to improve the reliability of the diagnostic systems. The first phase of the research activity was addressed to exploring the potential advantages of using multivariate classification /discrimination/anomaly detection methods on real world accelerometric condition monitoring data. The second phase consisted of an implementation into actual service of an innovative integrated multivariate health monitoring system based on a third-level multivariate processing of the condition indicators. A monitoring method which integrates several multivariate statistical techniques was developed and implemented in an efficient integrated tool. When applied to actual data collected on several helicopters in service, this method proved to be able to distinguish with very high level of statistical confidence true failure situations from false anomaly alerts that had been indicated as failures by other health monitoring systems.

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Submission Keywords: 
Multivariate statistics; Anomaly detection; Gear drives; Helicopters
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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