SILHIL Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines

Software-in-the-loop and Hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The creation of an offline framework for verifying and validating supervisory failure prognostics and decision making routines is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle

Improving Aircraft Engines Prognostics and Health Management via Anticipated Model-Based Validation of Health Indicators

The aircraft engines manufacturing industry is subjected to many dependability constraints from certification authorities and economic background. In particular, the costs induced by unscheduled maintenance and delays and cancellations impose to ensure a minimum level of availability. For this purpose, Prognostics and Health Management (PHM) is used as a means to perform online periodic assessment of the engines’ health status. The whole PHM methodology is based on the processing of some variables reflecting the system’s health status named Health Indicators. The collecting of HI is an on-board embedded task which has to be specified before the entry into service for matters of retrofit costs. However, the current development methodology of PHM systems is considered as a marginal task in the industry and it is observed that most of the time, the set of HI is defined too late and only in a qualitative way. In this paper, the authors propose a novel development methodology for PHM systems centered on an anticipated model-based validation of HI. This validation is based on the use of uncertainties propagation to simulate the distributions of HI including the randomness of parameters. The paper defines also some performance metrics and criteria for the validation of the HI set. Eventually, the methodology is applied to the development of a PHM solution for an aircraft engine actuation loop. It reveals a lack of performance of the original set of HI and allows defining new ones in order to meet the specifications before the entry into service

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

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

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)

Prognostic Reasoner based adaptive power management system for a more electric aircraft

This research work presents a novel approach that addresses the concept of an adaptive power management system design and development framed in the Prognostics and Health Monitoring(PHM) perspective of an Electrical power Generation and distribution system(EPGS).PHM algorithms were developed to detect the health status of EPGS components which can accurately predict the failures and also able to calculate the Remaining Useful Life(RUL), and in many cases reconfigure for the identified system and subsystem faults. By introducing these approach on Electrical power Management system controller, we are gaining a few minutes lead time to failures with an accurate prediction horizon on critical systems and subsystems components that may introduce catastrophic secondary damages including loss of aircraft. The warning time on critical components and related system reconfiguration must permits safe return to landing as the minimum criteria and would enhance safety. A distributed architecture has been developed for the dynamic power management for electrical distribution system by which all the electrically supplied loads can be effectively controlled.A hybrid mathematical model based on the Direct-Quadrature (d-q) axis transformation of the generator have been formulated for studying various structural and parametric faults. The different failure modes were generated by injecting faults into the electrical power system using a fault injection mechanism. The data captured during these studies have been recorded to form a “Failure Database” for electrical system. A hardware in loop experimental study were carried out to validate the power management algorithm with FPGA-DSP controller. In order to meet the reliability requirements a Tri-redundant electrical power management system based on DSP and FPGA has been develope

Advanced Diagnostics of Position Sensors for the Actuation Systems of High-Speed Tilting Trains

Trains tilting permits a train to travel at a high speed while maintaining an acceptable passenger ride quality with respect to the lateral acceleration, and the consequent lateral force, received by the passengers when the train travels on a curved track at a speed in excess of the balance speed built into the curve geometry. The tilting of a train carbody is performed by a control and actuation system which operates as a closed servoloop accepting the commands from the train control system, generating the torque necessary to tilt the carbody with respect to the bogie and measuring the tilt angle to close the control loop. Measurement of the tilt angle of each train vehicle is performed by two sensors located in the front and rear part of the vehicle. Since a correct tilt angle measurement is vital for the system operation and for ensuring a safe ride, in case of discrepancy between the signals of the two tilt angle sensors of any vehicle, the tilting operation is disabled and the train speed is reduced. An innovative tilt angle sensors health management system is herein presented that makes intelligent use of all available information to allow detection of malfunctioning of an individual tilt angle sensor, thereby enabling a continued operation of the tilting system and a high speed travel after a sensor failure occurs

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for continuous operations in machines which can also improve productivity and enhance system safety. In condition-based maintenance (CBM), effective diagnostics and prognostics are important aspects of CBM which provide sufficient time for maintenance engineers to schedule a repair and acquire replacement components before the components finally fail. All machine components have certain characteristics of failure patterns and are subjected to degradation processes in real environments. This paper describes a technique for accurate assessment of the remnant life of machines based on prior expert knowledge embedded in closed loop prognostics systems. The technique uses Support Vector Machines (SVM) for classification of faults and evaluation of health for six stages of bearing degradation. To validate the feasibility of the proposed model, several fault historical data from High Pressure Liquefied Natural Gas (LNG) pumps were analysed to obtain their failure patterns. The results obtained were very encouraging and the prediction closely matched the real life particularly at the end of term of the bearings

A Study of the Degradation of Electronic Speed Controllers for Brushless DC Motors

Brushless DC motors are frequently used in electric aircraft and other direct drive applications. As these motors are notactually direct current machines but synchronous alternating current machines; they are electronically commutated by a power inverter. The power inverter for brushless DC motors typically used in small scale UAVs is a semiconductor base delectronic commutator that is external to the motor and is referred to as an electronic speed control (ESC). This paper examines the performance changes of a UAV electric propulsion system resulting from ESC degradation. ESC performance is evaluated in simulation and on a new developed test bed featuring propulsion components from a reference UAV. An increase in the rise fall times of the switched voltages is expected to cause timing issues at high motor speeds. This study paves the way for further development of diagnostic and prognostic methods for inverter circuits which are part of the overall electric UAV system