Paper Info
Title
Fleet-based approach for tool wear estimation using sequential importance sampling with resampling
Abstract
In this paper, we study the use of historical data from a fleet (i.e., a group) of equipment to improve condition monitoring and health assessment for each individual equipment (within the fleet) for maintenance purposes. In particular, we propose a fleet-based approach to estimate the tool wear of milling machines at arbitrary operating conditions (OCs) using the Monte Carlo sequential importance sampling with resampling (SIR) algorithm. Unlike traditional data-driven estimation methods which usually rely on historical data available at the same OC, the proposed method can be used when there is a lack (or insufficiency) of historical data at the target OC by leveraging on the availability of historical data at others. First, we introduce a similarity measure based on the well-known extended Taylor's tool life equation to identify other OCs (where historical data are available) that are most "similar" to the target one. We then combine historical data from these OCs to construct a suitable model for the SIR to estimate the tool wear at the OC of interest. Our experimental results on a simulated dataset show that the proposed fleet-based approach with SIR can significantly improve tool wear estimation accuracy.
Year
DOI
Venue
2014
10.1109/ICARCV.2014.7064532
ICARCV
Keywords
Field
DocType
condition monitoring improvement,extended taylor tool life equation,manufacturing systems,tool wear estimation,milling machines,monte carlo methods,monte carlo sequential importance sampling-with-resampling algorithm,condition monitoring,health assessment improvement,similarity measure,sampling methods,arbitrary operating conditions,maintenance engineering,historical data,fleet-based approach,wear,estimation,hidden markov models,manufacturing,force
Data mining,Monte Carlo method,Importance sampling,Similarity measure,Computer science,Control engineering,Tool wear,Condition monitoring,Statistics,Hidden Markov model,Resampling
Conference
ISSN
Citations 
PageRank 
2474-2953
0
0.34
References 
Authors
7
2
Name
Order
Citations
PageRank
Tung Le131.56
Omid Geramifard2353.88