Speed learning on the fly

The practical performance of online stochastic gradient descent algorithms is highly dependent on the chosen step size, which must be tediously hand-tuned in many applications. The same is true for more advanced variants of stochastic gradients, such as SAGA, SVRG, or AdaGrad. Here we propose to adapt the step size by performing a gradient descent on the step size itself, viewing the whole performance of the learning trajectory as a function of step size. Importantly, this adaptation can be computed online at little cost, without having to iterate backward passes over the full data.Comment: preprin

Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction

The visual focus of attention (VFOA) has been recognized as a prominent conversational cue. We are interested in estimating and tracking the VFOAs associated with multi-party social interactions. We note that in this type of situations the participants either look at each other or at an object of interest; therefore their eyes are not always visible. Consequently both gaze and VFOA estimation cannot be based on eye detection and tracking. We propose a method that exploits the correlation between eye gaze and head movements. Both VFOA and gaze are modeled as latent variables in a Bayesian switching state-space model. The proposed formulation leads to a tractable learning procedure and to an efficient algorithm that simultaneously tracks gaze and visual focus. The method is tested and benchmarked using two publicly available datasets that contain typical multi-party human-robot and human-human interactions.Comment: 15 pages, 8 figures, 6 table

An Approach to the Health Monitoring of the Fuel System of a Turbofan

This paper focuses on the monitoring of the fuel system of a turbofan which is the core organ of an aircraft engine control system. The paper provides a method for real time on-board monitoring and on-ground diagnosis of one of its subsystems: the hydromechanical actuation loop. First, a system analysis is performed to highlight the main degradation modes and potential failures. Then, an approach for a real-time extraction of salient features named indicators is addressed. On-ground diagnosis is performed through a learning algorithm and a classification method. Parameterization of the on-ground part needs a reference healthy state of the indicators and the signatures of the degradations. The healthy distribution of the indicators is measured on field data whereas a physical model of the system is utilized to simulate degradations, quantify indicators sensibility and construct the signatures. Eventually, algorithms are deployed and statistical validation is performed by the computation of key performance indicators (KPI)

Methodology for the Diagnosis of Hydromechanical Actuation Loops in Aircraft Engines

This document provides a method for on-board monitoring and on-ground diagnosis of a hydromechanical actuation loop such as those found in aircraft engines. First, a complete system analysis is performed to understand its behaviour and determine the main degradation modes. Then, system health indicators are defined and a method for their real time on-board extraction is addressed. Diagnosis is performed on-ground through classification of degradation signatures. To parameterize on-ground treatment, both a reference healthy state of indicators and degradations signatures are needed. The healthy distribution of indicators is obtained from data and a physics-based model is used to simulate degradations, quantify indicators sensibility and construct the signatures database. At last, algorithms are deployed and a statistical validation of the performances is conducted

Diagnostics of an Aircraft Engine Pumping Unit Using a Hybrid Approach based-on Surrogate Modeling

This document introduces a hybrid approach for fault detection and identification of an aircraft engine pumping unit. It is based on the complementarity between a model-based approach accounting for uncertainties aimed at quantifying the degradation modes signatures and a data-driven approach aimed at recalibrating the healthy syndrome from measures. Because of the computational time costs of uncertainties propagation into the physics based model, a surrogate modeling technic called Kriging associated to Latin hypercube sampling is utilized. The hybrid approach is tested on a pumping unit of an aircraft engine and shows good results for computing the degradation modes signatures and performing their detection and identification

Methodology for the Diagnosis of Hydromechanical Actuation Loops in Aircraft Engines

This document provides a method for on-board monitoring and on-ground diagnosis of a hydromechanical actuation loop such as those found in aircraft engines. First, a complete system analysis is performed to understand its behaviour and determine the main degradation modes. Then, system health indicators are defined and a method for their real time on-board extraction is addressed. Diagnosis is performed on-ground through classification of degradation signatures. To parameterize on-ground treatment, both a reference healthy state of indicators and degradations signatures are needed. The healthy distribution of indicators is obtained from data and a physics-based model is used to simulate degradations, quantify indicators sensibility and construct the signatures database. At last, algorithms are deployed and a statistical validation of the performances is conducted