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Health monitoring of helicopter drive train components based on support vector data description

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. Such a method introduces an Anomaly Score (AS) as the combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CIs), thus implicitly including the operational variability in the model through the CI correlation. The problem of fault detection is thus recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal (healthy) and anomalous (faulty) observations. In this paper, a procedure based on an efficient one-class classification method, not requiring any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows for obtaining good data descriptions without the need of a significant amount of statistical data. Several analyses are carried out in order to validate the proposed procedure, using flight vibration data collected from a H 135 (formerly known as EC 135) servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarms rate and missed detection rates with respect to individual CI and to the ASs obtained assuming Gaussian-distributed CI has been analysed.