Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data
Saved in:
Main Authors: | Nor, A.K.M., Pedapati, S.R., Muhammad, M., Leiva, V. |
---|---|
Format: | Article |
Published: |
MDPI
2022
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124729775&doi=10.3390%2fmath10040554&partnerID=40&md5=a1656ffe9430765b994ee7e07163c7ff http://eprints.utp.edu.my/28669/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Overview of explainable artificial intelligence for prognostic and health management of industrial assets based on preferred reporting items for systematic reviews and meta-analyses
by: Nor, A.K.M., et al.
Published: (2021) -
Dynamic Failure Assessment Using Bayesian Methodology
by: Mohd Yusop, Nurazura
Published: (2011) -
Deep learning and explainable machine learning on hair disease detection
by: Heng, Wei Wei, et al.
Published: (2023) -
Bayesian Updating for Probability of Failure of Jacket
Platforms in Malaysia
by: Kurian, V.J., et al.
Published: (2013) -
Explainable Deep Learning Model for Cardiac Arrhythmia Classification
by: Abdullah, Talal AA, et al.
Published: (2022)