Data driven prognosis approach for safety critical systems

Venkatesh Kulkarni, Manju Nanda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Safety critical systems are being developed to improve the performance and cost effectiveness. The safety critical system are used in various domain such as aerospace domain, military, defense etc. In an aerospace domain there are many parameters affects the system environmental conditions, or hazards which cause many faults in the system which leads to failure. It is necessary to know before the system fails, so that necessary remedies can take to prevent the failure. The tool/software is needed to monitor the health management of safety critical systems. In this paper a prognostic technique is being used to mitigate the system failure. There are many techniques for the prognosis such as data driven technique, model based technique, and hybrid technique. This paper proposes implementation of the artificial neural network [ANN] based prognosis illustrates the use of data driven technique. The novelty of the proposed algorithm is that it uses formal techniques to develop a robust & reliable prognostics algorithm. The approach developed will be demonstrated for gyro sensor a critical component in the aerospace domain. The ANN can train and classify real data from the gyro sensors, and it is implemented using high level interpreted language GNU-Octave. The cost function/error function is calculated for the trained ANN data and it is being observed that the values are converging to the minimum value. At last the system is classified as healthy, partially healthy, and unhealthy state of the system.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1699-1703
Number of pages5
ISBN (Electronic)9781509007745
DOIs
Publication statusPublished - 05-01-2017
Externally publishedYes
Event1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Bangalore, India
Duration: 20-05-201621-05-2016

Conference

Conference1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016
CountryIndia
CityBangalore
Period20-05-1621-05-16

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Electrical and Electronic Engineering

Cite this

Kulkarni, V., & Nanda, M. (2017). Data driven prognosis approach for safety critical systems. In 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings (pp. 1699-1703). [7808123] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTEICT.2016.7808123