PHM Based Adaptive Power Management System for a More Electric Aircraft

ROBIN KUTTIKKADAN SEBASTIAN, Suresh Perinpinayagam, and Alireza Alghassi
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
Supporting Agencies (optional): 
HINDUSTAN AERONAUTICS LIMITED,BANGALORE,INDIA
AttachmentSizeTimestamp
ijphm_16_031.pdf1.37 MBNovember 15, 2016 - 4:15am

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. 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 was 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 developed.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
031
Page Count: 
13
Submission Keywords: 
MEA
AEA
PHM
IVHM
EPGS
Submission Topic Areas: 
Component-level PHM
Data-driven methods for fault detection, diagnosis, and prognosis
Health management system design and engineering
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
Physics of failure
  
 
 
 

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