TY - GEN
T1 - A Hypothesis testing approach to Zero-Fault-Shot learning for Damage Component Classification
AU - Bechhhoefer, Eric
AU - Matania, Omri
AU - Bortman, Jacob
N1 - Publisher Copyright: © 2023 Prognostics and Health Management Society. All rights reserved.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85178379843&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
A2 - Kulkarni, Chetan S.
A2 - Roychoudhury, Indranil
T2 - 15th Annual Conference of the Prognostics and Health Management Society, PHM 2023
Y2 - 28 October 2023 through 2 November 2023
ER -