A Data-Driven Maintenance Framework Under Imperfect Inspections for Deteriorating Systems Using Multitask Learning-Based Status Prognostics

A Data-Driven Maintenance Framework Under Imperfect Inspections for Deteriorating Systems Using Multitask Learning-Based Status Prognostics

This paper proposes a data-driven, condition-based maintenance framework (DCBM) for deteriorating equipment under the impact of varying environments and natural aging. The equipment's degradation status is determined by a prognostic and health monitoring method. Generally, monitoring data and m...

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Bibliographic Details
Main Authors: Lei Zhang, Jianguo Zhang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Condition-based maintenance
imperfect inspections
multitask learning
degradation assessment
probabilistic forecasting
status prognostics
Online Access:https://ieeexplore.ieee.org/document/9310204/

Internet

https://ieeexplore.ieee.org/document/9310204/

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