Abstract

One of the most significant research trends in the last decades of the aeronautic industry is the effort to move towards the design and the production of “more electric aircraft”. Within this framework, the application of the electrical technology to flight control systems has seen a progressive, although slow, increase: starting with the introduction of fly-by-wire and proceeding with the partial replacement of the traditional hydraulic/electro-hydraulic actuators with purely electro-mechanical ones. This evolution allowed to obtain more flexible solutions, reduced installation issues and enhanced aircraft control capability. Electro-Mechanical Actuators (EMAs) are however far from being a mature technology and still suffer from several safety issues, which can be partially limited by increasing the complexity of their design and hence their production costs. The development of a robust Prognostics and Health Management (PHM) system could provide a way to prevent the occurrence of a critical failure without resorting to complex device design. This paper deals with the first part of the study of a comprehensive PHM system for EMAs employed as primary flight control actuators; the peculiarities of the application are presented and discussed, while a novel approach, based on short pre-flight/post-flight health monitoring tests, is proposed. Turn-to-turn short in the electric motor windings is identified as the most common electrical degradation and a particle filtering framework for anomaly detection and prognosis featuring a self-tuning non-linear model is proposed. Features, anomaly detection and a prognostic algorithm are hence evaluated through state-of-the art performance metrics and their results discussed.

Highlights

  • Following the latest developments of the aviation industry, Electro-Mechanical Actuators (EMAs) are slowly replacing the traditional electro-hydraulic or hydraulic solution for fly-by-wire flight controls in military and civilian applications

  • Due to safety issues, their use as primary flight control actuators is still limited to experimental aircraft or UAVs (Jensen, Jenney & Dawson, 2000), (Derrien, Tievs, Senegas & Todeschi, 2011), (Roemer & Tang, 2015), while they are more rapidly advancing in nonsafe critical applications such as flap/slats control surfaces (Christmann, Seemann & Janker, 2010), (Recksieck, 2012)

  • Several research efforts can be found in the literature, addressing the electric motor (Nandi, Tolivat & Li, 2005), (Brown, Georgoulas, Bole, Pei, Orchard, Tang, Saha, Saxena, & Goebel, 2009), (Belmonte, Dalla Vedova & Maggiore, 2015), mechanical components (Balaban, Saxena, Goebel, Watson, Bharadwaj & Smith, 2009), (Balaban, Saxena, Narasimhan, Roychoudhury, Goebel & Koopmans, 2010), (Lessmeier, Enge-Rosenblatt, Bayer & Zimmes, 2014), (Van Der Linden, Dreyer & Dorkel, 2016) and Electronic Power Unit (EPU) (Brown, Abbas, Ginart, Ali, Kalgren & Vachtsevanos, 2010), (Li, Chen & Vachtsevanos, 2014)

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Summary

INTRODUCTION

Following the latest developments of the aviation industry, Electro-Mechanical Actuators (EMAs) are slowly replacing the traditional electro-hydraulic or hydraulic solution for fly-by-wire flight controls in military and civilian applications. The unpredictable nature of the command imposed to the actuators and the presence of random loads due to gust and turbulence make it difficult to extract significant data during flight, while the number of sensor has to be kept to a minimum to avoid cost increases and reliability issues. Both the monitored system and the degradation mode under study are heavily non-linear and affected by non-Gaussian noise; the prognostic algorithm must be able to mirror this behavior to limit the prediction uncertainty and maximize its accuracy

SYSTEM CONFIGURATION
SYSTEM MODEL
Electric drive model
Mechanical transmission model
Flight control surface model
HEALTH MONITORING STRATEGY
Feature selection
ANOMALY DETECTION
Operational scenario
Anomaly detection through data-driven approach
Anomaly detection through particle-filtering framework
PARTICLE FILTERING FOR PROGNOSIS
Remaining Useful Life prediction
Prognosis performance
SYSTEM LIMITATIONS AND FURTHER DEVELOPMENTS
Findings
CONCLUSIONS
Full Text
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