A predictive maintenance policy for manufacturing systems considering degradation of health monitoring device

•Modeling the impact of health monitoring device degradation state in measurement error.•Estimation of degradation state and RUL of the system from data observed by a degrading monitoring device.•Predictive maintenance policy considering the uncertainty of system state estimation and degradation sta...

Full description

Saved in:
Bibliographic Details
Published in:Reliability engineering & system safety 2024-08, Vol.248, p.110177, Article 110177
Main Authors: Dinh, Duc-Hanh, Do, Phuc, Hoang, Van-Thanh, Vo, Nhu-Thanh, Bang, Tao Quang
Format: Article
Language:English
Subjects:
Automatic
Engineering Sciences
Kalman filter
Maintenance decision-making
Monitoring device degradation
Monitoring device replacement
Predictive maintenance
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Modeling the impact of health monitoring device degradation state in measurement error.•Estimation of degradation state and RUL of the system from data observed by a degrading monitoring device.•Predictive maintenance policy considering the uncertainty of system state estimation and degradation state of monitoring device.•The applicability of the proposed models for an injection molding machine. Health monitoring device (HMD) providing the current condition for its monitored manufacturing system is often subjected to a gradual degradation process. The degradation of the HMD may lead to inaccurate system condition monitoring data, conducting to non-appropriate maintenance decision-making. This paper presents a novel predictive maintenance policy for manufacturing systems considering degradation of its HMD. To address this issue, a method based on Kalman filter is developed for estimating the system state. However, due to the data uncertainty given by statistic noise and HMD degradation impacts, the error between estimated value and true state is unavoidable. To overcome this issue, an adaptive predictive maintenance, in which both the estimated state of system and the current state of HMD are used for maintenance decision-making, is then proposed for the system and its HMD. A cost model is developed to find the optimal maintenance policy. A case study of injection molding machine is conducted to show the applications and the benefits of the proposed maintenance policy.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2024.110177