Physic-based vs data-based digital twins for bush bearing wear diagnostic

Industry is increasingly turning to predictive maintenance by using digital twins (DTs) to follow and predict evolution of mechanical system. This article presents, compares and discusses two DTs to diagnose wear of bush bearings under dynamic loads. The first DT is driven by a model based on data a...

Full description

Saved in:
Bibliographic Details
Published in:Wear 2023-08, Vol.526-527, p.204888, Article 204888
Main Authors: Regis, Amandine, Arroyave-Tobon, Santiago, Linares, Jean-Marc, Mermoz, Emmanuel
Format: Article
Language:English
Subjects:
Bush bearing
Diagnostic tool
Digital twin
Engineering Sciences
Materials and structures in mechanics
Mechanics
Wear
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Industry is increasingly turning to predictive maintenance by using digital twins (DTs) to follow and predict evolution of mechanical system. This article presents, compares and discusses two DTs to diagnose wear of bush bearings under dynamic loads. The first DT is driven by a model based on data analysis using statistical process control (SPC). The second DT is based on physical laws: Boussinesq's and Archard's equations. Both DTs are fed by data recorded on a test bench instrumented with sensors of temperature, acceleration and displacement. Rules for fault detection were identified, explained and applied to the two DTs implemented. The two implemented DTs detected abnormal wear behaviours. The data-based DT using the SPC is easier to implement and it detects change in wear behaviour earlier. In contrast, the physic-based DT has the advantage of being predictive, so it can be used when only the operating conditions of the system are known. This work is a contribution for new wear diagnostic tools. •Implementation of a digital twin to monitor wear of a bush bearing system.•Wear monitoring through data-based model and physic-based model.•Discussion on the fault detection capacity of the implemented digital twins.
ISSN:0043-1648
1873-2577
DOI:10.1016/j.wear.2023.204888