Fault detection in operating 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. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then 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 and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, 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 alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Peer reviewedPostprin

Investigating Conformance Monitoring Issues in Air Traffic Control Using Fault Detection Approaches

In order to maintain Air Traffic Control (ATC) system safety, security and efficiency, conformance monitoring must be performed to ensure that aircraft adhere to their assigned clearances. New Decision Support Tools (DSTs), coupled to advanced communication, navigation and surveillance technologies are being developed which may enable more effective conformance monitoring to be undertaken relative to today. However, there are currently no general analysis techniques to help identify fundamental conformance monitoring issues and more effective approaches that new DSTs should employ. An approach to address this need is presented in this work that draws parallels between ATC conformance monitoring and general system fault detection, allowing fault detection methods developed for other domains to be employed for this new application. The resulting Conformance Monitoring Analysis Framework provides a structure to research conformance monitoring issues and approaches. Detailed discussions are presented for each of the elements of the framework, including the Conformance Basis, Actual System Representation, Conformance Monitoring Model, Conformance Residual Generation and Decision-Making components. Flight test data during a simple lateral non-conformance maneuver was used to demonstrate various implementation options of the framework. Application of the framework for ATC conformance monitoring research was demonstrated using flight test and simulator data in various operational and surveillance environments. Key findings in the lateral, vertical and longitudinal domains during non-transitioning and transitioning flight regimes are presented. In general, it was found that more effective conformance monitoring can be conducted relative to existing systems in the non-transitioning environments when advanced surveillance systems provide higher accuracy, higher update rate and higher order dynamic state information for use in more sophisticated DST algorithms. This is contrasted to the significantly greater conformance monitoring challenges that exist in the transitioning regimes due to Conformance Basis and modeling uncertainties. These challenges can be handled through the use of procedural design, higher fidelity modeling techniques or the surveillance of intent states. Two extended applications of the framework are also presented: a method for intent inferencing to determine what alternative trajectory a non-conforming aircraft may be following and a technique for environmental parameter estimation.This work was funded by the NASA Langley Research Center under grant NAG1-02006. Sincere thanks to Richard Barhydt & Mark Ballin for their support through this grant. The authors would also like to thank Mike Paglione at the FAA Technical Center and Len Tobias at the NASA Ames Research Center for earlier support through the FAA/NASA Joint University Program under grant FAA95-G-017

Control optimization, stabilization and computer algorithms for aircraft applications

The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory

Crack detection using nonlinear output frequency response functions - an experimental study

The new concept of Nonlinear Output Frequency Response Functions (NOFRFs) is introduced in this paper to detect cracks in beams using frequency domain information. The results show that the NOFRFs are a sensitive indicator of the presence of cracks providing the excitation is of an appropriate strength. The new results provide a novel and effective method for the detection of cracks in beams, with applications in structural fault diagnosis