A Hypothesis testing approach to Zero-Fault-Shot learning for Damage Component Classification

Eric Bechhhoefer, Omri Matania, Jacob Bortman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Often, in condition monitoring, datasets are asymmetric. That is, for most machines being monitored, there is no labeled fault data, only nominal data (hence, the dataset is asymmetric). Deep Learning and other neural network-based mechanization have difficulty solving this type of problem, as they typically require a full set of labeled data, both nominal and faulted. Zero-Fault Shot learning is a class of machine learning problems with no labeled fault training data. In this class of problems, only nominal data is used for knowledge transfer. In this paper, a mixed hypothesis testing and Bayes classifier it used to provide both inferences to the type of fault and also provide information as to when maintenance should be provided. This is done without any fault data and demonstrates knowledge transfer from a set of nominal components, greatly reducing the cost of implementation and fielding of a system.

Original languageAmerican English
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
EditorsChetan S. Kulkarni, Indranil Roychoudhury
Edition1
ISBN (Electronic)9781936263059
StatePublished - 1 Jan 2023
Event15th Annual Conference of the Prognostics and Health Management Society, PHM 2023 - Salt Lake City, United States
Duration: 28 Oct 20232 Nov 2023

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Number1
Volume15

Conference

Conference15th Annual Conference of the Prognostics and Health Management Society, PHM 2023
Country/TerritoryUnited States
CitySalt Lake City
Period28/10/232/11/23

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'A Hypothesis testing approach to Zero-Fault-Shot learning for Damage Component Classification'. Together they form a unique fingerprint.

Cite this